{"as_of":"2026-08-10T08:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e360d06afea61b789d261716d07a02c45739325476e1bcb2f003fb5b0081aac7","coverage":[{"denominator":91,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":91,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:07:49.703372Z","state":"measured"},{"denominator":92,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":92,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T11:06:46.449195Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.20033","snapshot_observed_at":"2026-08-04T11:06:46.449195Z","title":"Emonet-face: An expert-annotated benchmark for synthetic emotion recognition,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.07355","last_updated":"2026-05-28T11:55:17Z","snapshot_observed_at":"2026-08-07T20:52:44.589895Z","submitted_at":"2025-10-08T14:13:28Z","title":"AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T11:06:46.449195Z"},"links":{"cited_paper":"/paper/2505.20033","citing_paper":"/paper/2510.07355"},"observation_digest":"sha256:e43cd4d8bcd7ba905fcbd226351bfa4615e7f0c03fcde67d4ea6297880295960","observation_id":"6a3bd3b4-2cb7-481c-9ffb-82fbbd7114b7","resolution":{"observed_at":"2026-08-04T11:06:46.449195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.20033/citation-record","integrity":"/paper/2505.20033/integrity","json":"/paper/2505.20033/citation-record.json","paper":"/paper/2505.20033"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:40.707456Z","title":"Learning to generate 3d stylized character expressions from humans","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:40.707456Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:89ed749f8d3907a382f430f0f80172ba4f5ad1c3f86c92427a08abfd2302d484","observation_id":"61519e75-2782-4d87-91de-e6ee16cd36aa","resolution":{"observed_at":"2026-08-07T14:07:40.707456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:40.737271Z","title":"Modeling stylized character expressions via deep learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:40.737271Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:016918589d675683c2f606915d123318d5f4565ae9c0fcfd8401dbbc14942b05","observation_id":"886fb16b-8406-49fd-8a6b-287c67e5fd47","resolution":{"observed_at":"2026-08-07T14:07:40.737271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:40.789499Z","title":"Are emotions natural kinds?Perspectives on Psychological Science, 1(1):28–58, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:40.789499Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:7a5b03829c8f01d10259194e356acf546614f2d67b90b8002efaf18ef1dfc5d4","observation_id":"73c01c8d-e59c-461d-9bd3-99c7210e7006","resolution":{"observed_at":"2026-08-07T14:07:40.789499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:40.884355Z","title":"Houghton Mifflin Harcourt, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:40.884355Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:118c4dd77271eca3fe165ecc97ee855e3e92db70799578ff9cf3fffe4386b2e0","observation_id":"28f16d32-9aa5-4521-a934-887379970dde","resolution":{"observed_at":"2026-08-07T14:07:40.884355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:40.996457Z","title":"Fabian Benitez-Quiroz, Ramprakash Srinivasan, and Aleix M","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:40.996457Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:a0c8d70a2ff816fb513069766436ea82018b3851f6f7e20cd677a8d67a80a3e7","observation_id":"80820629-077b-4cfd-8aa9-0e2ebb96b6e1","resolution":{"observed_at":"2026-08-07T14:07:40.996457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.047869Z","title":"Flux.1-dev","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.047869Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:63fce2990f06160cabccff0f388ebc23a965c5e6b29490626426e2ee847f2a30","observation_id":"3ed93cd4-bb68-433a-8a3a-09ea425f7357","resolution":{"observed_at":"2026-08-07T14:07:41.047869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.123023Z","title":"Umiltà, and Vittorio Gallese","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.123023Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:cf03941475dd678284b2742dc634a031dd63a1f8dc5a5f4913a947ada3458902","observation_id":"a18cae38-9826-419e-9837-51570ec04f11","resolution":{"observed_at":"2026-08-07T14:07:41.123023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.194324Z","title":"Daryl Cameron and Michael Inzlicht","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.194324Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:d5bc4564a95ec33455f4a0021afbc4c352f22563350daa8a254efbfc2b756a43","observation_id":"59a68f68-23e6-431b-b028-2c0ea2e5bcff","resolution":{"observed_at":"2026-08-07T14:07:41.194324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.268997Z","title":"Character Technologies","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.268997Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:50103056428742e2615f21e2ef256bd38d25d95aad8620e0914479feb1a1d21b","observation_id":"0c08de1d-9bb8-4018-a8e8-78cc5acc3c69","resolution":{"observed_at":"2026-08-07T14:07:41.268997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.337023Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.337023Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:0bdc4bef11cbcd5578064b04150240118e09fc001867f5052da1a5bc4bc5bd9f","observation_id":"9a8f8ad0-f106-43ac-bd34-5ce1a143473c","resolution":{"observed_at":"2026-08-07T14:07:41.337023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:41.413580Z","title":"Cowen and Dacher Keltner","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.413580Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:e7536a1196f88c9a965678c39dd50eeeb6ec43f90170725dfc7a4ec60abc9c4f","observation_id":"91a3dadc-7f53-4481-b578-e134c476e800","resolution":{"observed_at":"2026-08-07T14:07:41.413580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:04.341733Z","title":"Davidson","venue":null,"work_id":"874960ec-7ef5-48e5-856b-1848f8a0d92f","year":2003},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.471281Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:fbcf7a36e46a5fc2c28e1327365e54571318ea8115d80be8347b90fd507ac143","observation_id":"00c60447-60e9-4e98-a4b3-2a0687f4b512","resolution":{"observed_at":"2026-08-07T14:08:04.427586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:04.158574Z","title":"An argument for basic emotions.Cognition and Emotion, 1992","venue":null,"work_id":"5f45be05-402f-4392-a1b1-e6011d7a5e66","year":1992},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.524918Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:6840f4f5dd8dcc6378bb4169160c675d925020b1f809beac38a82f7e152c1534","observation_id":"748835bb-0527-4273-b7f3-3c4dbd56fe21","resolution":{"observed_at":"2026-08-07T14:08:04.225475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:03.978707Z","title":"Basic emotions","venue":null,"work_id":"e1928733-2a8e-4269-8bcb-9d7cee362e76","year":1999},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.619186Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1c38a0708892ee5098f67be96d98328016211c9fcd747bd92445d345745115e4","observation_id":"7bb2710b-c093-4b04-9f4d-0657d525cece","resolution":{"observed_at":"2026-08-07T14:08:04.063316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:03.820274Z","title":"What people think ai should infer from faces","venue":null,"work_id":"680fec33-3c6c-4634-82ad-ccb916946e01","year":2022},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.698066Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:dce5535e4e584a855fcca620c1ce60108e1f9a82c3f5e0c5ab5f492db5c24ea7","observation_id":"921ade10-fc42-4b16-8176-386378ffc1c2","resolution":{"observed_at":"2026-08-07T14:08:03.897439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:03.572412Z","title":"How well does chatgpt perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment","venue":null,"work_id":"40a28691-df01-4bed-a981-32ddd97e32a6","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.777034Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:69d427a81164de2a94c1bef2197e75f494732fd16a7e16e240b1a55f4245b2f5","observation_id":"b61b28c0-1b2a-4a48-9c42-ef8a422779c3","resolution":{"observed_at":"2026-08-07T14:08:03.683344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:03.318606Z","title":"Multi-pie.Image and Vision Computing, 28(5):807–813, 2010","venue":null,"work_id":"c9f90310-5673-4c14-8e6c-3da323cf3c99","year":2010},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.847901Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:4bb64420265697b042fc9d2579d54ab3dbae82aba4b5c8e5ab9733f13c84767c","observation_id":"e96e7a47-1dc6-4ce6-b9ab-c9f495bdfd54","resolution":{"observed_at":"2026-08-07T14:08:03.432472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:03.035125Z","title":"Identifying implicit social biases in vision-language models","venue":null,"work_id":"1212cee0-2e63-4003-9c1b-30cfd532f09d","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:41.910712Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:624a0dffa81466d9239df2fd3b1087a262f0802f45235d972f1c73690df7da3f","observation_id":"c2ba7693-2aa9-4792-a5da-96e6220f2663","resolution":{"observed_at":"2026-08-07T14:08:03.198534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:02.722795Z","title":"A chinese face dataset with dynamic expressions and diverse ages synthesized by deep learning","venue":null,"work_id":"ddb99488-1284-4e35-b4e4-c513ca510999","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.006374Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:d64d30e74fdd3b73cbd1337bcb2dbae8708075948270466534a7871c5521e9f4","observation_id":"ff120bc9-cf1e-4bb0-baa4-b2a3523737df","resolution":{"observed_at":"2026-08-07T14:08:02.864917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:02.439240Z","title":"Izard.The Face of Emotion","venue":null,"work_id":"661822c5-e272-4b9b-8613-1fc9f72d299c","year":1971},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.107385Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:bc46e8cb261d97598eff8b0900053989a05693f8ce291de32e580acacaf4566d","observation_id":"dbb3e754-e774-4ad1-b025-89734c173d22","resolution":{"observed_at":"2026-08-07T14:08:02.601568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:02.134296Z","title":"Izard.The Psychology of Emotions","venue":null,"work_id":"4349bba9-abb1-42da-b898-7ee97b3745d3","year":1991},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.163041Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1a25f53af303f5f17ab49741f6e8f1ba32b574da8789077e56a8e1298c9d0a08","observation_id":"54a03a59-9108-42cd-9aad-f8dd2c3c0d17","resolution":{"observed_at":"2026-08-07T14:08:02.278491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.983264Z","title":"Kajiwoto: Create custom ai companions","venue":null,"work_id":"c8d036e8-f008-4d42-8765-b94b622d7d75","year":2022},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.219824Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3a0c6e0f8d061c449c922db380a8dbd3e0a42ca4c77e123640a575a76414d805","observation_id":"abfe77e4-e45f-43a7-9b26-d5d017bd6d44","resolution":{"observed_at":"2026-08-07T14:08:02.057169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.845296Z","title":"Chatgpt and mental health: Friends or foes?Health Science Reports, 2024","venue":null,"work_id":"5ee5181e-1088-40c4-9dad-01d876cc11a0","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.283681Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:b346fc51935addaea6b889d6ffa704a9e309f904d9d21e5f1d87e174f9184773","observation_id":"821ae140-ac1e-48ba-b14f-ac4996c5a7b7","resolution":{"observed_at":"2026-08-07T14:08:01.908550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.720982Z","title":"Alvarez, Adria Recasens, and Agata Lapedriza","venue":null,"work_id":"cb6ebdd8-c5a8-4ef8-b618-2e23b96d719d","year":2017},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.336909Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:f263866f432442903441d3f4bff8f769cbf2b1c282f766e5cafc47ac9dc909f7","observation_id":"c63220e7-ddfe-4518-a907-09c298ab6cb8","resolution":{"observed_at":"2026-08-07T14:08:01.776196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.568393Z","title":"Orpheus: Emotionally expressive speech ai","venue":null,"work_id":"6547a584-ca50-444a-b446-f7347702091c","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.397883Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:7d587d6814f9c94d78d296258af7fe0c4a2cf01938afe8d6c9c05c80adc38139","observation_id":"c0ddbd74-0401-4554-bfd2-6757ee991f40","resolution":{"observed_at":"2026-08-07T14:08:01.673791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:42.441939Z","title":"Lazarus.Emotion and Adaptation","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.441939Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:bcba263dacaf86ae595ffd769e4e7d09a1d5861455cced18662fb349128b61ae","observation_id":"2d84ef61-f072-4254-8c9e-d87c680a75e8","resolution":{"observed_at":"2026-08-07T14:07:42.441939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.415523Z","title":"Lewis, J","venue":null,"work_id":"2f525c39-3266-4986-9afa-ace9990fb012","year":2016},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.514208Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:59eae0dbb06fb7a36589c6fd82765cb74d19142cb6f1e1c7e735cf5e7007c1ea","observation_id":"e4bd48e7-85c7-4223-90ec-9b9cbc370f89","resolution":{"observed_at":"2026-08-07T14:08:01.468217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.253281Z","title":"Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews","venue":null,"work_id":"63892232-be69-4c0b-af48-a5ece9597cc3","year":2010},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.600721Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:5c60012fa9c0fa22e5fb163df59231f812dac7a541b4a9faf9f42ad656f97da9","observation_id":"f1d52541-ade0-4507-a74f-31a7bc2c3e4d","resolution":{"observed_at":"2026-08-07T14:08:01.337067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:01.114522Z","title":null,"venue":null,"work_id":"18187fd9-be2f-4b69-a6d5-379ab97dba31","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.684521Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:8ce29e6d0c110e7e7712b2bd16f27872059e9aa053a253e4922fa9abf8e428ab","observation_id":"9830e0a2-0a4c-4fca-afad-afa0ef2e6a13","resolution":{"observed_at":"2026-08-07T14:08:01.194294Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.980087Z","title":"Coding facial expressions with gabor wavelets","venue":null,"work_id":"fdb76a7e-19c6-4ea5-99ae-0d1a03b46a5c","year":1998},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.746395Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:53b04529442465f2e0a29c8c5b7bf61a8b8befd288ceb1c72467eab1af3ee471","observation_id":"4ad3374f-bc78-4556-9b95-3cb083d59ec7","resolution":{"observed_at":"2026-08-07T14:08:01.040586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.834107Z","title":"Dataset diversity: Measuring and mitigating geographical bias in image search and retrieval","venue":null,"work_id":"922cd534-8cd2-4027-afed-c29756cae985","year":2021},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.748965Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:f862ad6a39ef153d8651f946f2ce510f8154a19de2f947a422285220f514b00b","observation_id":"b96ad82e-f180-4ac7-ad6f-686abd5bdc0f","resolution":{"observed_at":"2026-08-07T14:08:00.917432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.685862Z","title":"Midjourney","venue":null,"work_id":"e8b1a622-e265-41d9-8b71-f7fa04466a3a","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.752372Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:dee54efdd118e3e82537b73316c9328ec588eb09d7d8d463b7bcd1530c22345b","observation_id":"88ccd3c7-9484-4d11-afa9-61b14fed3950","resolution":{"observed_at":"2026-08-07T14:08:00.745893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.577684Z","title":null,"venue":null,"work_id":"5dd5e36e-1953-45b9-8844-24d049384922","year":2017},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.800387Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:7e3169e258225df534801fc9966211846d452f9e3540bf591653e2f63927fe39","observation_id":"59ade027-f5ee-4797-ab60-5ee51d382fcb","resolution":{"observed_at":"2026-08-07T14:08:00.624382Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.434490Z","title":"Chatgpt.https://openai.com/chatgpt, 2023","venue":null,"work_id":"49bed9c9-2546-4d2e-a549-32a6acb0a331","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:42.931150Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:ffbee3fc12a389fcb0d338e5bbef34f59ddc83998498e301d968bad937997c82","observation_id":"756fdce6-efbd-4e57-a997-6d6b4332e7f7","resolution":{"observed_at":"2026-08-07T14:08:00.507289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.294533Z","title":"Gpt-4o: Advanced multimodal ai","venue":null,"work_id":"f950faf3-fb55-4e2e-9fec-fd94c93a28a5","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.053118Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:086b24ad397739551c72fb9f22176a1baf4f83b1902fb8a3f5321ee3a58c0274","observation_id":"1a0e90ef-ec11-49fe-8251-20b8574d9dfd","resolution":{"observed_at":"2026-08-07T14:08:00.358081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.157023Z","title":"Clore, and Allan Collins.The cognitive structure of emotions","venue":null,"work_id":"1d437cf0-3b2c-4955-99ca-6aa872f86deb","year":1990},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.099711Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:d45da40d0f746fc7e8936902647332774903ce8dfe0084107b3ad5d0432f3a96","observation_id":"00691525-f4e9-4b3b-b56b-d9698722b45b","resolution":{"observed_at":"2026-08-07T14:08:00.213193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:08:00.045677Z","title":"Oxford University Press, 1998","venue":null,"work_id":"13ed93cc-a046-422a-9d90-a0b69c4d9ec7","year":1998},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.219845Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:08e8ddd01c6556d930c4ca976d93d7f099eb7bc50baf83035d837f98eff5c44d","observation_id":"fbdc0598-4553-4371-850e-29086279a21a","resolution":{"observed_at":"2026-08-07T14:08:00.100197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.932945Z","title":"Gerrod Parrott.Emotions in Social Psychology","venue":null,"work_id":"37a01975-8443-444e-8e9e-931a08f4daf2","year":2001},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.396862Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1d76f05eddd5a647a53d88635780bd7e6db240619eda1ec4a9e1adf6ebf7db96","observation_id":"7fa39da8-b53b-473b-aba6-f38e0436d4a4","resolution":{"observed_at":"2026-08-07T14:07:59.985066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.788230Z","title":"Picard.Affective Computing","venue":null,"work_id":"bba2c82f-de3a-4996-9d13-24aff981a17e","year":1997},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.458488Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:046448bd269a2cba711ef65943590a5cbfab43c22fe7b4ca82c8dc48a8562f42","observation_id":"0e33a778-db19-4ce7-a422-8614b2238312","resolution":{"observed_at":"2026-08-07T14:07:59.860513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.632089Z","title":"Academic Press, 1980","venue":null,"work_id":"b822b37f-37ac-4691-a008-84c7a4b9b82d","year":1980},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.563242Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:ac27ddaef10c737333e4702ee57a2f2a7eabac1760818eac1159c3bf083e86c8","observation_id":"27cd35b6-c339-4af8-a108-109a581a16c1","resolution":{"observed_at":"2026-08-07T14:07:59.711268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.469934Z","title":"Two contrasting data annotation paradigms for subjective nlp tasks","venue":null,"work_id":"39553414-9d5f-41c5-8d80-bf330fc0ddc4","year":2022},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.735604Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:d3b070409c48561afed3badd989970492723af7db2bdcc3cbfb22362aefa4b41","observation_id":"5adf054a-ed13-4474-8c81-20e5db09f99c","resolution":{"observed_at":"2026-08-07T14:07:59.558674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.274753Z","title":"Msts: A multimodal safety test suite for vision-language models, 2025","venue":null,"work_id":"9683d1b7-d9c9-42d1-95e1-30509ec79e94","year":2025},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:43.900589Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:825ae1bc06253562c5dd4b24b2ca5da7ac2a04dd530999c81ec23f1d4835a1b4","observation_id":"d80b1edb-6005-41e1-afc7-87420c1b6a31","resolution":{"observed_at":"2026-08-07T14:07:59.376581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:59.110614Z","title":null,"venue":null,"work_id":"566237d5-cf6e-4018-8126-68cbedf10dc8","year":1962},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.028246Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:a011463dc814c2601d236c6e88915a332647d7180f8d7175672245e2a3e3797e","observation_id":"3e0f9c5d-75d3-44c9-8c36-4627840805e0","resolution":{"observed_at":"2026-08-07T14:07:59.206004Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.973924Z","title":"Sesame: Conversational speech model","venue":null,"work_id":"74fc0956-532a-4e86-a076-75329bd68c23","year":2024},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.209923Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:5f5028c16bec71984509b474e73c9df6acfa2ed39319e6ba64a4cbbecb4309b8","observation_id":"d1740e4e-37a3-457b-8857-b644bb9c2aad","resolution":{"observed_at":"2026-08-07T14:07:59.044646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.851681Z","title":"Shiota, Samantha L","venue":null,"work_id":"3f6ad3e9-e552-46bc-a88a-e1757826c214","year":2017},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.332183Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:fd281bb40da829206b1f78274d0de37ef0a3db5fe6a692e1eeb714f2c1cc2fc2","observation_id":"877decd2-b250-430a-bf44-a3ce050c7fbb","resolution":{"observed_at":"2026-08-07T14:07:58.903058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.701904Z","title":"Ai for mental health: A systematic review of affect recognition techniques.IEEE Transactions on Affective Computing, 14(1):3–20, 2023","venue":null,"work_id":"78b90478-0f7f-440b-9664-e1edd989e495","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.518065Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:7ea5ca4eaad95b5adf289fca25e9a64602b6a416a9aeb5b85a1dc626d48a7146","observation_id":"b8172943-f58a-4207-8f06-8bf4a1c89bb0","resolution":{"observed_at":"2026-08-07T14:07:58.772406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.519268Z","title":"Chatgpt: Opportunities, risks and priorities for psychiatry.Asian Journal of Psychiatry, 2023","venue":null,"work_id":"b6babd8a-adb3-4670-a203-422b5d394736","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.644786Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:300129ee4b46263d1b4f5c983724b262670af50e2b28abb1cb2e7969d45d0f90","observation_id":"5987db0f-91d4-4e71-bf7c-88ac22aaabe5","resolution":{"observed_at":"2026-08-07T14:07:58.604050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.346221Z","title":"Weidman and Jessica L","venue":null,"work_id":"8613ea8a-6874-454b-a073-95afe91f2811","year":2020},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.751398Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:16792eff48b25177332d16cf48b29d1f1507b8eaaee9b59726498c9161a9da09","observation_id":"a9e068f5-57a9-4a6e-b99d-9d217c1910f5","resolution":{"observed_at":"2026-08-07T14:07:58.422886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.192019Z","title":"The influence of background on facial emotion perception: A psychophysical study.i-Perception, 14(2), 2023","venue":null,"work_id":"8aaa0781-cfe8-4d64-b4a8-92ec3b4cf39e","year":2023},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:44.880083Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:e493716ff2fba8560d0be836b0e501f6a349577fb4e67242ca76b97327874218","observation_id":"dd161a3d-404e-4d03-ab94-116c3e9d837d","resolution":{"observed_at":"2026-08-07T14:07:58.276404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:58.077704Z","title":"emotion circuits","venue":null,"work_id":"2d4be36c-4f20-407c-9c50-db89d86e2132","year":2021},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.012662Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:0ee2b2e321b59aebf610390a4a8d9ddb104ce65aecc4433a45d2af84e67eebb3","observation_id":"68bda2c8-f8e0-4f23-9ef1-37374ed10709","resolution":{"observed_at":"2026-08-07T14:07:58.147281Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:57.887062Z","title":null,"venue":null,"work_id":"22ec44d8-4f67-4d97-a745-41f091a0cfc5","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.150803Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:9bfe64996583f4d362dd9ab26f59aa68c9d4aa591945f9c644596401e9f0069d","observation_id":"79ed708a-f51c-400b-bd79-558f89d5bc52","resolution":{"observed_at":"2026-08-07T14:07:57.990391Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:57.697803Z","title":null,"venue":null,"work_id":"21653531-6e08-4402-a3ca-df8942345a8f","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.245849Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:cac600e322ccbb82ee0039123cf3a6dea824dc95533e940a9463218edc50b5e4","observation_id":"36e6896b-0601-4328-8c6a-8f5bc0c2eda7","resolution":{"observed_at":"2026-08-07T14:07:57.790019Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:57.499021Z","title":null,"venue":null,"work_id":"a268d1e5-0a97-4697-8f7d-f0d116ac1747","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.379599Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:680a1d134db67f3e1ac0712e8b9816892d30d40134551f9965efbe759ecf4fab","observation_id":"7f20690d-07ef-4dbe-b245-597cd3a58c6f","resolution":{"observed_at":"2026-08-07T14:07:57.599169Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:57.323162Z","title":null,"venue":null,"work_id":"8916799a-96c2-4706-ae40-6fa29e4d1fa4","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.485723Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:610c8b2bbc56280283bfdb7239d8384fd664e24062c08c19651fb921ccc215e8","observation_id":"d5f054f6-95e7-424a-8636-b6652fb9c069","resolution":{"observed_at":"2026-08-07T14:07:57.377131Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:57.127356Z","title":null,"venue":null,"work_id":"624935f4-a996-4890-bae8-598b7c4e3aa0","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.585238Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1ab1c7f7de06c8e64cd18837d4eb2da8e90ffa46e2fa9f1bbc1f7f2c844897bc","observation_id":"71437857-3c54-4132-9f1d-ca46d8190872","resolution":{"observed_at":"2026-08-07T14:07:57.229981Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.928103Z","title":null,"venue":null,"work_id":"91a8fb9d-f299-4370-93ba-0a7214551b0b","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.757341Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:d441a1acb816ddbb9e03af024ef563283e25082df1240f665aa837886dbaecb7","observation_id":"6f77b6a8-c3c5-4149-a4bd-f32d4e872df2","resolution":{"observed_at":"2026-08-07T14:07:57.025468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.747667Z","title":null,"venue":null,"work_id":"f402586d-c734-47de-90ee-4a27e10c1f42","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:45.883177Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:edd86365828380193263f372160107b63fa10183bce431130f817d60bddff2e9","observation_id":"16dd5093-31ab-43c5-ba17-b42dd721ca0f","resolution":{"observed_at":"2026-08-07T14:07:56.837735Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.593247Z","title":null,"venue":null,"work_id":"89f9a781-55d9-43f5-a8c4-72f3c84c9d46","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.079429Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:4ae2bd1b541148582c3c7fdb11204a0314f4e631a0dc04cc24776443d5dc1079","observation_id":"d1052938-19ec-41d7-bcff-5f8af6ee79af","resolution":{"observed_at":"2026-08-07T14:07:56.672408Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.404077Z","title":null,"venue":null,"work_id":"be3d3fc4-b7d0-4cfd-8902-3bc3be2dcebe","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.208685Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:c8fdf7bc52d8af6207eb3d516f388f6e10a08ab47985946a417aa5eb5cda0e59","observation_id":"7c5a8b89-bed9-40d8-975d-662ae8c5fc0e","resolution":{"observed_at":"2026-08-07T14:07:56.490962Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.277984Z","title":null,"venue":null,"work_id":"f8e62885-ea87-4f9e-a7a1-d0f0757a133a","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.345709Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:fb3282c35a40127e8dbdc7bdfb65c9989d5b1f6a666fa56f10fab8b7d3df2f8c","observation_id":"5a852f09-6a60-4eb4-b8f4-488c01f30bd3","resolution":{"observed_at":"2026-08-07T14:07:56.332303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:56.012999Z","title":null,"venue":null,"work_id":"6b93af0c-6fc7-4372-afae-5d2e5939ae9c","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.445407Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:04fef98992449a65f0602cf2a7f04232fe34a7f88ca6e04f14d73da0a755b9ec","observation_id":"1cf036c4-34de-4e27-a605-a348105880f9","resolution":{"observed_at":"2026-08-07T14:07:56.132561Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:55.896792Z","title":null,"venue":null,"work_id":"b255c9f5-7e3a-4659-b27d-df62a7a27a9f","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.588994Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:b84100300ff2551ab1565bea6c5d9ef59237a18a4894c9b4ff07b6c925d7615e","observation_id":"eeeb348a-028f-448c-a72e-aeedbc1b84ac","resolution":{"observed_at":"2026-08-07T14:07:55.939813Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:55.572188Z","title":null,"venue":null,"work_id":"5a51cd2e-c0df-4caa-a1c4-15b2cc3ae8d9","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.742224Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3e1da5a46fd83dcaaae1430e631b22cdf25432d7e177f53b33531cb272c242cc","observation_id":"9af63453-cce2-4198-8c51-01b4ead874c7","resolution":{"observed_at":"2026-08-07T14:07:55.739577Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:55.210707Z","title":null,"venue":null,"work_id":"9996c3e6-b15c-4495-bd27-59d285e0212d","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:46.894197Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:e7ea479ab50018b7a29b3824df8c497545e768f1376e163d70e2b9fa4705a3ad","observation_id":"f0e15a38-85ad-44b4-9f2c-08a273361dea","resolution":{"observed_at":"2026-08-07T14:07:55.395826Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:55.026369Z","title":null,"venue":null,"work_id":"5e745806-0ea3-4633-a709-45ddf8e9a4e8","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.041414Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3a7731a69fe6954f18ab6cab7b32e4c1f0bc63b3226e81d93031c1e2cca0ace4","observation_id":"36d2db1d-3441-48ba-863a-c62d4b7ef5d0","resolution":{"observed_at":"2026-08-07T14:07:55.105325Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:54.922755Z","title":null,"venue":null,"work_id":"1facdaf3-d084-4736-9f5f-01966d8bf0b5","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.111331Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:49916d7d2a08ac6333208121cb38b855fbe617bf51cf7390b2b7760d12cc8222","observation_id":"aa3e965b-eee0-4f2b-aba8-914e1369d87e","resolution":{"observed_at":"2026-08-07T14:07:55.016440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:54.766778Z","title":null,"venue":null,"work_id":"2095b676-94db-441e-9318-84aa8e0e5b0f","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.213231Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:5ff7b54191a108c12237a797b20abd6b0c2c8caff99affdf467113298ef735b8","observation_id":"451eab49-80b5-4bd1-a24a-8987ecba701a","resolution":{"observed_at":"2026-08-07T14:07:54.827453Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:54.510477Z","title":null,"venue":null,"work_id":"803802c5-69f0-4faa-a852-c91e35f99787","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.301650Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1f7c6e6d114c6afaeb9e2590219c890fb100e7b48ca37153f86e483c94e14426","observation_id":"2b90435c-c554-4db1-bb7a-c0cc5d77e0ff","resolution":{"observed_at":"2026-08-07T14:07:54.633285Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:54.278680Z","title":null,"venue":null,"work_id":"e7384b72-9e61-4fff-a7ca-e6becf0b12c3","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.437982Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:921d5078e607f24415a4d3da84934aad0226a322b037ba4db75805f6348c5148","observation_id":"baa1bfbd-726c-41c8-8e07-0a6a3aed8041","resolution":{"observed_at":"2026-08-07T14:07:54.398788Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:54.090655Z","title":null,"venue":null,"work_id":"b1fc24a6-88f9-4353-9f0c-ebdcf7b11c31","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.534273Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:e48a08d58a953e6c00940ed806105cb864fc627ea2498a827baa2d5c49398241","observation_id":"022f0a48-340a-4881-8187-44b3dfcc0f06","resolution":{"observed_at":"2026-08-07T14:07:54.210333Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:53.918876Z","title":null,"venue":null,"work_id":"91099d7c-b74c-46af-a1d6-44609d5a79c2","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.665618Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:f1a8e4f3aa9bdeb4fcc238bc96974077d1c2d70b2614515c5a5072f52f6fd381","observation_id":"b6274dcb-d7e2-4ab8-97fd-d79845e1ede7","resolution":{"observed_at":"2026-08-07T14:07:54.014747Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:53.711420Z","title":null,"venue":null,"work_id":"a15c82fe-841d-4b1e-9b4b-a2e2ea171f15","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.777315Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:9d22b19df2d87f06b4a34b2985244482ff916162339b73eae61e572514b79743","observation_id":"c530e485-7534-47f9-a49a-64ae03cad321","resolution":{"observed_at":"2026-08-07T14:07:53.808967Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:53.493283Z","title":null,"venue":null,"work_id":"81709d7e-94b8-4864-a902-d99bb309d1c6","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:47.893951Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:8fe3e4ff686f2d4f33332628070155b6a2ca5080b7e8c0358ffc3cf8b5af9733","observation_id":"2b712011-bfab-4576-9221-34905cfa7e43","resolution":{"observed_at":"2026-08-07T14:07:53.602198Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:53.319828Z","title":null,"venue":null,"work_id":"4c788338-e19c-48cc-a8c2-e537045aeb60","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.004953Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:1ef20c7a9c0320b06dbfe1208f38029054fefb1a653e38e497aaa1f823b3be65","observation_id":"78872a04-4442-427e-a1a8-635e1a9974b8","resolution":{"observed_at":"2026-08-07T14:07:53.401621Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:53.068092Z","title":null,"venue":null,"work_id":"5d4e00b2-7d9a-4d3c-900d-31ddd3082b96","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.081562Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:353573c8ff3bf604a3d5c6cfb10304b41c867607e9c8c2514528c1ea8a914927","observation_id":"a285a845-3b3c-4b7c-bcda-12de2b4fa357","resolution":{"observed_at":"2026-08-07T14:07:53.182412Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:52.766351Z","title":null,"venue":null,"work_id":"891b7374-2008-4112-a3c0-a9a647683cee","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.140960Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:f9a61f45ae131982e3c73fe1e798171436064ddf777449ebba2bf3a115cf111d","observation_id":"fa43c79d-f20d-4efc-854d-370798859ec6","resolution":{"observed_at":"2026-08-07T14:07:52.910574Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:52.522514Z","title":null,"venue":null,"work_id":"392fa080-ffa2-44c3-b2a9-4b4251cdafd1","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.191158Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:8bfbc5ac996c35a6fa6f7629854be81b9b8c5ff9895d628cdb221ef903c24eb0","observation_id":"3224794f-5438-4473-8db4-a1cef94f761d","resolution":{"observed_at":"2026-08-07T14:07:52.646246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:52.267630Z","title":null,"venue":null,"work_id":"6689d5b1-cc27-4ef5-8b7f-8b01dfdee3a4","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.302171Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:5a1cc7ed0a1925f9490c340caa0d3a5391ff37bb4d1316eec67191d3c38356f3","observation_id":"33ce0bb3-28db-4941-bbfa-a3ae93fd3840","resolution":{"observed_at":"2026-08-07T14:07:52.418856Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:52.045433Z","title":null,"venue":null,"work_id":"8a491682-88e5-4566-9e5e-e04d95b68be7","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.393370Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:63a047848d7b4a42383cf4bf5c75c0216f4c2dda90004d7ff3f4849109a84bb4","observation_id":"92aa721b-6f4f-4387-b4cb-523e0e070f0b","resolution":{"observed_at":"2026-08-07T14:07:52.154750Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:51.763994Z","title":null,"venue":null,"work_id":"abf876c9-9a05-4dc5-a6ad-ed3e7e5215df","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.510365Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:35dbab7d7b45e3e9144eb756ae7dc13d542baee5d21c1968f3a78cec716d99af","observation_id":"1d4e4794-e356-41eb-a6aa-689513cac5a2","resolution":{"observed_at":"2026-08-07T14:07:51.912030Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:51.531525Z","title":null,"venue":null,"work_id":"4ff1a1bc-1e0f-4890-b4f4-1fe79df29c0f","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.622853Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:6e10770102cdf6286fba200d4983069d0dce5a84b0d62a0824d4b78482ed9dce","observation_id":"27f58076-4a78-46fe-8b28-c13dca799ac3","resolution":{"observed_at":"2026-08-07T14:07:51.645702Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:51.369994Z","title":null,"venue":null,"work_id":"55544e0e-4483-4eda-b8a5-25986f6e5021","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.737856Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3e2a8e49daeb08b79d34d77715d35796b2b25f968807745c22ed5038d354502b","observation_id":"3fd1d0c7-ce45-4ea1-95e6-2bafaad75e39","resolution":{"observed_at":"2026-08-07T14:07:51.455756Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:51.205845Z","title":null,"venue":null,"work_id":"3b97b3ef-246e-476d-af48-dabbec1a4727","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.827528Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:14543766c2452071f5752af658ab6a9d4b8690a3ef0fdf4406be1789bc78c3df","observation_id":"c5844b43-6edc-4fd4-ba0b-9c0726db35a8","resolution":{"observed_at":"2026-08-07T14:07:51.305605Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:51.027097Z","title":null,"venue":null,"work_id":"5401fba1-cdae-4d35-9408-bcf3038fa5cc","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.901163Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:e72894c9a05288e9afb8c53f7b26e50ed72f2cf2a21ebe0fbcd16a2ea8712338","observation_id":"e32f3464-f0f1-4a88-bcbc-6c5aa43a71f6","resolution":{"observed_at":"2026-08-07T14:07:51.149460Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:50.844330Z","title":null,"venue":null,"work_id":"2743553a-7597-40a1-a3d0-9648f9fc1270","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:48.985778Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:07e0c81bef1fdfb376bc0490a0f156478cdec6d02a1cbb0cf2d888533b9a9b34","observation_id":"e26d1049-feb7-4489-a2d0-b5a944968cd5","resolution":{"observed_at":"2026-08-07T14:07:50.913456Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:50.679891Z","title":"Fatigue/Exhaustion ’fatigue’, ’exhaustion’, ’weariness’, ’lethargy’, ’burnout’, ’Weariness’","venue":null,"work_id":"528e3f09-be10-4e13-9ded-823de3da6252","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.117661Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3347c554d34622711c8db1d081decb6d864ff1b7c6980984058e7fd84f50afe0","observation_id":"ccd0acb3-ae37-4c83-a8a6-c4370e067afc","resolution":{"observed_at":"2026-08-07T14:07:50.761298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:50.515008Z","title":null,"venue":null,"work_id":"98d59db8-3fc5-4999-9829-bc14761c8383","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.272072Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:3541de975d64f8f1f3373395bddf6dbc4fc10ceb630e71ab34e0d2b525eeec94","observation_id":"cf634d80-420c-4efd-97be-9cdd0823f21b","resolution":{"observed_at":"2026-08-07T14:07:50.623808Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:50.316396Z","title":null,"venue":null,"work_id":"15393504-d8e5-4bdb-9fe8-2e3cda0f34a9","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.364949Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:5ac695c0d4173ecf641f63f073601d72937f382d3dafb4d825b52527dbd347eb","observation_id":"da7e17c9-e24f-41b6-9c7a-fa4e9ec74d6b","resolution":{"observed_at":"2026-08-07T14:07:50.404043Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:50.152522Z","title":"16 Figure 6: Heatmap of Average Pairwise Weighted Kappa (κw, quadratic weights) Across All Emotions","venue":null,"work_id":"90ba763a-f7d6-4a03-a976-58889036acc3","year":2000},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.487360Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:489e7d3dd92d1e2f1bdeb4cb051f64c9dd1a01e47ecfee7693e4733314d6e05d","observation_id":"55400dc0-829c-4c7d-b584-6f71effe6c9a","resolution":{"observed_at":"2026-08-07T14:07:50.228199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:49.986800Z","title":"embarrassment","venue":null,"work_id":"5d4e4828-8408-41d3-99f3-06bb34b37cae","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.588320Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:dad0e09f23581138d6fabb74cbb42a81dd4f30627a1c028a171c714628ca70b6","observation_id":"ce4ccae0-e7f3-4cd4-b91e-2ffe3b0a15ce","resolution":{"observed_at":"2026-08-07T14:07:50.073466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:07:49.818137Z","title":"hint\" was provided to the model. For example, if an image was generated with","venue":null,"work_id":"85c69348-2f9b-4910-99aa-d475d7990be7","year":null},"citing_paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T14:07:49.703372Z"},"links":{"citing_paper":"/paper/2505.20033"},"observation_digest":"sha256:83f419ee99c9cecc154a8c067569e680e836c2111348136cb36ec99df59175d5","observation_id":"dd1f153b-85d1-41c9-bf01-b1d6a7a8fba8","resolution":{"observed_at":"2026-08-07T14:07:49.906930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.20033","last_updated":"2025-05-27T07:26:21Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T13:00:46.007301Z","submitted_at":"2025-05-26T14:19:58Z","title":"EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition"},"reference_resolution":{"displayed":91,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":91},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 1 inbound Pith citation observation for arXiv:2505.20033."}