{"as_of":"2026-08-17T18:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9991e357d3560a802c6dc57dfe6c88179b56c42989289b4131361ea72225eb29","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T19:38:16.112128Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2508.12198/citation-record","integrity":"/paper/2508.12198/integrity","json":"/paper/2508.12198/citation-record.json","paper":"/paper/2508.12198"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1505.00468","last_updated":"2016-10-27T03:50:19Z","snapshot_observed_at":"2026-08-15T14:20:20.896398Z","submitted_at":"2015-05-03T20:07:39Z","title":"VQA: Visual Question Answering","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.00468","snapshot_observed_at":"2026-08-05T19:38:16.044435Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.044435Z"},"links":{"cited_paper":"/paper/1505.00468","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:44dca4c6535f3d45ef7e8ea6851cff4eaead9437b6c33c2a87fb7ae45b0df246","observation_id":"ffc02770-64c8-49ef-b2f0-a33291934cc1","resolution":{"observed_at":"2026-08-05T19:38:16.044435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02737","last_updated":"2025-02-04T21:43:16Z","snapshot_observed_at":"2026-08-14T05:03:05.661970Z","submitted_at":"2025-02-04T21:43:16Z","title":"SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02737","snapshot_observed_at":"2026-08-05T19:38:16.048986Z","title":"Burtenshaw, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.048986Z"},"links":{"cited_paper":"/paper/2502.02737","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:312f256f968a1fe68ba66774f08b9e86f52c5d1281067f7a36f3eab4f50fae09","observation_id":"7fba6398-4f1f-4d80-b1d1-b894b2c3301d","resolution":{"observed_at":"2026-08-05T19:38:16.048986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02153","last_updated":"2025-09-15T22:15:00Z","snapshot_observed_at":"2026-08-12T13:48:47.535846Z","submitted_at":"2025-06-02T18:35:16Z","title":"Small Language Models are the Future of Agentic AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.02153","snapshot_observed_at":"2026-08-05T19:38:16.053371Z","title":"Heinrich, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.053371Z"},"links":{"cited_paper":"/paper/2506.02153","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:6ba63904e343e074f92561edee68f66ee6dccb6275f2f237d414611ee67050cf","observation_id":"74bc3e6d-4652-4e7c-a883-7796216781b2","resolution":{"observed_at":"2026-08-05T19:38:16.053371Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:38:16.444762Z","title":"Louradour, R","venue":null,"work_id":"e7dee73e-321c-4727-aacc-d3700f8caf6b","year":2009},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.057848Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:796181f612e88fbd34846863db7a85820f20946ca29eb9c1217f38d258f98de7","observation_id":"0b794cc3-fe79-4f07-8632-395333b46e4a","resolution":{"observed_at":"2026-08-05T19:38:16.546294Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T19:38:17.012053Z","title":"M., 2006: Pattern Recognition and Machine Learning","venue":null,"work_id":"5155e48b-2d93-4f3d-89ec-193acae6fb8d","year":2006},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.061669Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:4f2dd5b076d7ad2e6f0762e06696ff3c408dc0968072fc76193ae48882bb1c7d","observation_id":"5edc4e76-a978-41ef-9744-8d87eb89adca","resolution":{"observed_at":"2026-08-05T19:38:17.151298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03556","last_updated":"2016-06-17T04:39:01Z","snapshot_observed_at":"2026-08-14T21:53:12.782100Z","submitted_at":"2016-06-11T05:41:10Z","title":"Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?","version":2},"cited_work":{"arxiv_id":"1606.03556","doi":null,"metadata_source":"pith","pith_arxiv_id":"1606.03556","snapshot_observed_at":"2026-08-05T19:38:16.224607Z","title":"Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?","venue":"cs.CV","work_id":"6a109c69-17e5-4447-9f0a-3190f26570d3","year":2016},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.066052Z"},"links":{"cited_paper":"/paper/1606.03556","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:0e0fe03ffac1b9faf5a00a3154fdaffe8d911231e9326448d67ddab18e79f5d8","observation_id":"93275e92-eb94-4c00-98b3-0d38b65b59dc","resolution":{"observed_at":"2026-08-05T19:38:16.241263Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T19:38:16.821898Z","title":"L., 1959: Introduction to Theoretical Meteorology","venue":null,"work_id":"58d21c22-268d-4a90-b23a-883512416c6b","year":1959},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.071415Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:8d8360f52325b3cf5d3a26bd6861c2a011617b907d95887ebc029275b1b097f6","observation_id":"54e0e3a0-8285-4c42-a6e2-c29623db4650","resolution":{"observed_at":"2026-08-05T19:38:16.892689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-17T18:04:53.578114Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-05T19:38:16.075600Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.075600Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:cd9f51fab2deb5d34afd17428735f88f887debbddfc9d3f54b8979443c110a91","observation_id":"cdc5d33b-05de-4c97-94e7-a89cc052b89c","resolution":{"observed_at":"2026-08-05T19:38:16.075600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11401","last_updated":"2021-04-12T15:42:18Z","snapshot_observed_at":"2026-08-07T05:44:30.677502Z","submitted_at":"2020-05-22T21:34:34Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11401","snapshot_observed_at":"2026-08-05T19:38:16.079544Z","title":"Perez, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.079544Z"},"links":{"cited_paper":"/paper/2005.11401","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:3d1c66a6795ad0b58a3e73298eeada517c9da67cac3570a2e955c39d9aded422","observation_id":"4a608d7f-d341-44b9-bc95-1a233460d61f","resolution":{"observed_at":"2026-08-05T19:38:16.079544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05299","last_updated":"2025-04-07T17:58:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-07T17:58:57Z","title":"SmolVLM: Redefining small and efficient multimodal models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05299","snapshot_observed_at":"2026-08-05T19:38:16.083173Z","title":"Zohar, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.083173Z"},"links":{"cited_paper":"/paper/2504.05299","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:3922414e517bc290de2b0f2c5962ec1ff6cafab347ff63c3a1f31ef318ec029d","observation_id":"52070b9a-9bda-4190-bd98-2f2bb6b8afaf","resolution":{"observed_at":"2026-08-05T19:38:16.083173Z","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":"2026-08-05T19:38:16.087022Z","title":"B., Jr., 1962: The serial position effect of free recall","venue":null,"work_id":null,"year":1962},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.087022Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:e8434e114759539b262952f0b5d179e21f052ad7fbaaa7b807fa306b7bdf54d7","observation_id":"09daca64-12c2-411f-9ed3-39d18f35f007","resolution":{"observed_at":"2026-08-05T19:38:16.087022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04664","last_updated":"2020-05-28T20:51:40Z","snapshot_observed_at":"2026-08-09T01:09:42.786424Z","submitted_at":"2020-03-10T12:38:31Z","title":"Automatic Curriculum Learning For Deep RL: A Short Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04664","snapshot_observed_at":"2026-08-05T19:38:16.090533Z","title":"Colas, L","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.090533Z"},"links":{"cited_paper":"/paper/2003.04664","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:0b90e19ee791c57a844e3d195ac4eadcd008e0c1d5ed779b156477b24943e325","observation_id":"2a797309-cda9-4104-bff6-5cea08aaa5e5","resolution":{"observed_at":"2026-08-05T19:38:16.090533Z","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":"2026-08-05T19:38:16.094024Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.094024Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:170718aa19794d35964170ebb925117b636b6e4c918779a3d6b02a2330d14007","observation_id":"3b189d4f-1234-4794-abaa-fedfd7713168","resolution":{"observed_at":"2026-08-05T19:38:16.094024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.05158","last_updated":"2016-09-23T17:16:37Z","snapshot_observed_at":"2026-08-14T21:39:29.592639Z","submitted_at":"2016-09-16T17:58:14Z","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.05158","snapshot_observed_at":"2026-08-05T19:38:16.097354Z","title":"Caballero, F","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.097354Z"},"links":{"cited_paper":"/paper/1609.05158","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:6b220734127346d87eaec5e69a80f100a028b5bee5d30ef61234931f6bba0369","observation_id":"e570f4ce-8f82-4d67-be81-baa6da774c01","resolution":{"observed_at":"2026-08-05T19:38:16.097354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-08-17T01:19:18.409791Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-05T19:38:16.100876Z","title":"Shazeer, N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.100876Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:4c6bd2676128dbe35edfd8543b188ab3d0768cfb16301975cb18ced170650240","observation_id":"fdc7fe45-bc03-4dba-9880-f9452e5fac2d","resolution":{"observed_at":"2026-08-05T19:38:16.100876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-08-13T07:04:41.220509Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-05T19:38:16.104175Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.104175Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:21bc7afd11022a1e677eea69fbdd3115c21abc90ddd2d17959fe442813b90e32","observation_id":"a3409153-8f16-4ce9-b1ec-e97715e14118","resolution":{"observed_at":"2026-08-05T19:38:16.104175Z","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":"2026-08-05T19:38:16.642767Z","title":"S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction","venue":null,"work_id":"4377d0e3-b42a-4eea-8a6d-7d3781d3edf6","year":1995},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.108521Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:83860106303e3bf04d8dbcb97406ef78a4d1c8becba2d406ad59e161cc95d6eb","observation_id":"468883d2-82b2-42b6-ab54-82a2aef925b0","resolution":{"observed_at":"2026-08-05T19:38:16.700130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-05T19:38:16.112128Z","title":"Bundy, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T19:38:16.112128Z"},"links":{"citing_paper":"/paper/2508.12198"},"observation_digest":"sha256:ff0cf07d9b507373d8e8f7439e6a48105e7b40cdf7b1b3daa6a3ff22a98c414f","observation_id":"02f1a2a0-761c-45c1-81c8-757d69524cb4","resolution":{"observed_at":"2026-08-05T19:38:16.112128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.12198","last_updated":"2025-08-17T01:36:31Z","latest_version":1,"primary_category":"physics.ao-ph","snapshot_observed_at":"2026-08-09T01:09:59.322543Z","submitted_at":"2025-08-17T01:36:31Z","title":"Exploring Multimodal AI Reasoning for Meteorological Forecasting from Skew-T Diagrams"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":2,"verified_fuzzy":3},"total_outbound_references":18},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2508.12198."}