{"as_of":"2026-08-20T00:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b6dfa6d6978afc538762802fd6629af955536eabdbf30f0cefd5469b4e7319ab","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T15:29:17.330034Z","state":"measured"},{"denominator":62,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":62,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2411.14219/citation-record","integrity":"/paper/2411.14219/integrity","json":"/paper/2411.14219/citation-record.json","paper":"/paper/2411.14219"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T15:29:17.962995Z","title":null,"venue":null,"work_id":"535cd0c1-0b64-431b-9489-5ad92db035ef","year":null},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.157185Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:5f07f73d77cefff36447d468a2904f491e26bb44020463224545416c1798ad0f","observation_id":"90b67600-ac11-40c4-a00d-71c6d3b0f76e","resolution":{"observed_at":"2026-08-12T15:29:17.965534Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.955648Z","title":"Snap happy: camera traps are an effective sampling tool when compared with alternative methods,","venue":null,"work_id":"a0dc5c43-bcad-4201-8c23-39f3407f64e8","year":2019},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.160891Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:0e72001cbda9c5fccbdc8a0208ab1f47db34cd12a7a6faea2dd16bf305bead89","observation_id":"e88e13a2-669b-47d3-942a-f275141bdfdb","resolution":{"observed_at":"2026-08-12T15:29:17.958286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.948547Z","title":"Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks,","venue":null,"work_id":"21fa1f20-6356-45de-a64d-6600eb1b256d","year":2017},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.164050Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:11852a4448e6c3672bddef95c4bb8384b8ed8ac1b56276b53712c00bcc01597f","observation_id":"399a3f99-1098-4ac5-9c7c-866fa3d1a036","resolution":{"observed_at":"2026-08-12T15:29:17.951112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.941107Z","title":"Software to facilitate and streamline camera trap data management: A review,","venue":null,"work_id":"be34ec25-5d03-4f00-9ff5-c97af1c00d1a","year":2018},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.167371Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:9ea1a762a28f6e9e1ce61dd3891bac66e472df28724639009aae4fffa10a41c6","observation_id":"1b7b72f1-acc5-45f5-95bb-abfe8bcb29c7","resolution":{"observed_at":"2026-08-12T15:29:17.943864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.933085Z","title":"Advances in image acquisition and processing technologies transforming animal ecological studies,","venue":null,"work_id":"6986cec8-a577-4e2a-9762-6d3e7e45aac8","year":2021},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.170581Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:a3cfc493df1babc981771cacafa47b1953edbfa2d905425dfed8f01f19a6a649","observation_id":"46d8dd6a-0aac-4850-8183-a0021daefb1c","resolution":{"observed_at":"2026-08-12T15:29:17.936250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.848628Z","title":"Component processes of detection probability in camera - trap studies: understanding the occurrence of false-negatives,","venue":null,"work_id":"8c0f6023-07e3-4058-88b4-8b21a388a56f","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.173719Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:b2d9a2342f8866977f848b5571eb413eb842db850c60b3d545a7885c4ecb36b3","observation_id":"9b7da433-121f-42dc-8c57-568f2486aba0","resolution":{"observed_at":"2026-08-12T15:29:17.851218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.840689Z","title":"Recommended guiding principles for reporting on camera trapping research,","venue":null,"work_id":"e43afc54-6475-40c0-aef0-6667b0aafe91","year":2014},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.176888Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:bef0c8f82cda239450d20154cf2579efffd72888612a445d215dc14de09db110","observation_id":"419d37b9-0a47-43ca-a3ab-c3fec875db04","resolution":{"observed_at":"2026-08-12T15:29:17.843740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.833122Z","title":"You only look once: Unified, real -time object detection,","venue":null,"work_id":"c72a5346-106d-4b7b-9201-4ed790036e83","year":2016},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.180127Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:f3d56a2540c25af932b979e9886d1b66c01c56574b921512872711affcf51f1e","observation_id":"37aca3df-e971-4347-beb4-c8271322ea38","resolution":{"observed_at":"2026-08-12T15:29:17.836028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.825176Z","title":"Best practices and software for the management and sharing of camera trap data for small and large scales studies,","venue":null,"work_id":"66319f8a-33eb-43cc-b3b9-bb3bd41c2155","year":2017},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.182526Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:dad453ce0b365cde88f8eb2d419e6592a65e8579de889725b916f15acc8ff08f","observation_id":"ba1e421d-818b-495c-9594-10d124ebdaeb","resolution":{"observed_at":"2026-08-12T15:29:17.828287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.817699Z","title":"Snapshot Serengeti, high - frequency annotated camera trap images of 40 mammalian species in an African savanna,","venue":null,"work_id":"6de086f9-1511-4406-ad6c-9f321ccc0219","year":2015},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.184934Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:9013f86f50cd851714d2377cc1305ae2e7c0b3f8525ed3a917f2126ef671c864","observation_id":"9e386eb0-edce-4034-8254-dcdc2c9b76e4","resolution":{"observed_at":"2026-08-12T15:29:17.820411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.810064Z","title":"Planning for success: identifying effective and efficient survey designs for monitoring,","venue":null,"work_id":"60870a78-d7ac-4b1f-bb25-f109b4fcbe17","year":2011},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.187418Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:f18435c0f66a75d02d427c0e46b5b01c151ad06612193025c8f738be223103c5","observation_id":"2c2b7626-5889-444f-8c7a-6a892a907db4","resolution":{"observed_at":"2026-08-12T15:29:17.812835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.802404Z","title":"A novel method to reduce time investment when processing videos from camera trap studies,","venue":null,"work_id":"ea3c7d9a-3220-4bc1-aace-ed106e6fb55e","year":2014},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.189958Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:554527caf4b15d3341ac4d8061d06d24356448572dffd2a316f502b6448d45d8","observation_id":"072528ec-ed97-453a-98a1-583835a9a35a","resolution":{"observed_at":"2026-08-12T15:29:17.805273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.793340Z","title":"R: a language for data analysis and graphics,","venue":null,"work_id":"c6feb39a-8704-440a-a573-f3bec5e9abfc","year":1996},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.192423Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:a1d7d42f0c4f132f8739364691001096870ff9177fa197ad8988c9465a552704","observation_id":"612d47e4-a12a-4a46-a372-9752838fd837","resolution":{"observed_at":"2026-08-12T15:29:17.797150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.194757Z","title":"Efficient pipeline for camera trap image review. arXiv,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.194757Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:9ee27519f0c075d7a7ff447012e5cbdbc484dee37e57bf9b70dc325576f77b49","observation_id":"46316d80-253f-495d-8667-6318c254d9bf","resolution":{"observed_at":"2026-08-12T15:29:17.194757Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-12T15:29:17.784578Z","title":"Fennell, C","venue":null,"work_id":"50b71bee-199a-4627-ba15-38bb7139af30","year":2022},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.197556Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:3e2d846108eed558e28c96ba6f614b45396b90dab81adb0399eb6f44bb8bf0df","observation_id":"4e247457-2b03-46c9-b4ba-9b5fbaa2d60a","resolution":{"observed_at":"2026-08-12T15:29:17.787473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.776701Z","title":"Object detection in 20 years: A survey,","venue":null,"work_id":"5ca79a7e-7b63-4989-a42c-e0c001a60cdd","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.200291Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:6416b422fed73eb2e90532d95f13469ef3e04c32ade9fe689380da1bf610ab51","observation_id":"a371141a-d790-449d-916c-ca818d36d77f","resolution":{"observed_at":"2026-08-12T15:29:17.779198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.769254Z","title":"Biodiversity studies: science and policy,","venue":null,"work_id":"af3f80d3-221d-469f-a1e6-6b854d2a8239","year":1979},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.202909Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:165e033f4d90407a9529f0f4fc5433668759c729f3997c4c68aeb929d49eb416","observation_id":"a5615b0d-19ea-4f40-b6e3-eea205d7e741","resolution":{"observed_at":"2026-08-12T15:29:17.771969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.760099Z","title":"Enhancing biodiversity conservation and monitoring in protected areas through efficient data management,","venue":null,"work_id":"ead7267a-7df5-4097-a771-04b61c691708","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.205564Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:0a811c845f6d23596504e3f750b0856496fe6097fde6af61dbe36b5e1e7cb261","observation_id":"f81b90d3-a638-4977-b464-9663c19ea344","resolution":{"observed_at":"2026-08-12T15:29:17.763173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.752277Z","title":"Ecoinformatics: supporting ecology as a data -intensive science,","venue":null,"work_id":"d0529bba-f148-4aeb-a60c-5a60b770aac8","year":2012},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.208316Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:69ed98020a7b8c43b24d589b28adfa56a6447032dff76b3014a4ab9bebfe1f0a","observation_id":"72c3dc63-de3a-40b3-85fc-53524001fe5a","resolution":{"observed_at":"2026-08-12T15:29:17.754805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.744450Z","title":"Object detection with deep learning: A review,","venue":null,"work_id":"3b589f6c-8923-46ce-aab0-52a98c22857c","year":2019},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.210859Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:6d580b373ab530fd8ca4c289041f87d036805a89e5a25b58ce83d8f7d5e04f98","observation_id":"dd04e092-c6ae-49f4-8e30-c3372d8ca9e8","resolution":{"observed_at":"2026-08-12T15:29:17.747096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10523","last_updated":"2024-08-30T09:13:31Z","snapshot_observed_at":"2026-08-16T13:22:42.102721Z","submitted_at":"2024-08-30T09:13:31Z","title":"Harnessing Artificial Intelligence for Wildlife Conservation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10523","snapshot_observed_at":"2026-08-12T15:29:17.213241Z","title":"Harnessing Artificial Intelligence for Wildlife Conservation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.213241Z"},"links":{"cited_paper":"/paper/2409.10523","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:04fb8f60eea808d94c422bf8e99b16a79293f0a6b8271936f064256b04a996c2","observation_id":"64ea47d9-5541-4844-b689-bd0e8c96486a","resolution":{"observed_at":"2026-08-12T15:29:17.213241Z","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":"2026-08-12T15:29:17.734073Z","title":"Empowering wildlife guardians: an equitable digital stewardship and reward system for biodiversity conservation using deep learning and 3/4G camera traps,","venue":null,"work_id":"adeaffeb-c5bc-42ce-a103-4a00cc0ef23f","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.216068Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:332cbc6afce1e84d2549cc099190c357acdc0750672b7635fe2106c970e1d0de","observation_id":"ad96df31-7971-4884-99a9-7e21d3e151ad","resolution":{"observed_at":"2026-08-12T15:29:17.738332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.725735Z","title":"Deep learning object detection methods for ecological camera trap data,","venue":null,"work_id":"51343a2c-a136-4452-b8c7-d6aae80e38bf","year":2018},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.218950Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:120623f583af172ff8c4ce9a07a19b068099af78f2f11a253ab141eafcc4205d","observation_id":"a31dc696-a41d-4401-bb9e-57d8fe542ebd","resolution":{"observed_at":"2026-08-12T15:29:17.728846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.718424Z","title":"A comprehensive overview of technologies for species and habitat monitoring and conservation,","venue":null,"work_id":"f72b775a-a983-4f3e-9a8d-24889a0b4881","year":2021},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.221860Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:bf4db60ca9a97035c413993852603539b8c7f8ce2d86dfba1afb5da8a47345f8","observation_id":"a0810006-d99a-461e-8167-e63fba1d6103","resolution":{"observed_at":"2026-08-12T15:29:17.720981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13549","snapshot_observed_at":"2026-08-12T15:29:17.224843Z","title":"A survey on multimodal large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.224843Z"},"links":{"cited_paper":"/paper/2306.13549","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:7079534838c82365361c009fb9f79837decc79307dbbc6069fb486653b4aac14","observation_id":"37ab9fbb-b06e-4126-ab62-f2bb91e3f316","resolution":{"observed_at":"2026-08-12T15:29:17.224843Z","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":"2026-08-12T15:29:17.711161Z","title":"Contextual object detection with multimodal large language models,","venue":null,"work_id":"4ad42202-626f-439a-b338-e5148833c33c","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.227564Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:b5d872919e21165ab4bb9e4d2b5df80bdb3664391dd8f4b9d4d5f70e35d6017a","observation_id":"27750434-15f6-4bd9-aeb2-894c1523fc71","resolution":{"observed_at":"2026-08-12T15:29:17.713772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.703584Z","title":"Learning to prompt for vision-language models,","venue":null,"work_id":"79312853-d3f1-459d-a1b3-4782448c10a4","year":2022},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.230741Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:a4903da728318b817b5354fe106f1bbf68a3969dc0bba94996717db8c993efe1","observation_id":"9d4077b0-4b41-4e6c-b60d-7fd82a0979d4","resolution":{"observed_at":"2026-08-12T15:29:17.706318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.696550Z","title":"Pre -trained language models and their applications,","venue":null,"work_id":"44a22ac8-2a89-4990-9c61-bad7892433a1","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.233944Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:37be429ea6a0ec3129cbf7939b40f52a25b315b1989b0c8c8a6e57e4c2b152d7","observation_id":"6f03db34-2f46-416f-b189-1a7459a432e6","resolution":{"observed_at":"2026-08-12T15:29:17.699049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.689026Z","title":"Vcoder : Versatile vision encoders for multimodal large language models,","venue":null,"work_id":"ab6b6184-34bb-4cec-8a2e-863f9abed5dd","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.237477Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:b49c6990cefd83922353599e2d441ee15ff9df7fb24a608f35163fd42a22bed3","observation_id":"d7109b7c-a461-48c9-8d50-232e87d95642","resolution":{"observed_at":"2026-08-12T15:29:17.691941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.679271Z","title":"Visionllm: Large language model is also an open-ended decoder for vision-centric tasks,","venue":null,"work_id":"a94f4bf9-f33d-422b-adee-588aac9fcdbb","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.240818Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:224fab3970400c5b52aa9a5b3d3e8128f8c546a0066440f1db96d0e512c842da","observation_id":"1ba5c428-2df9-4993-ba86-20fc177e7f75","resolution":{"observed_at":"2026-08-12T15:29:17.682896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.670981Z","title":"Seeing what is not there: Learning context to determine where objects are missing,","venue":null,"work_id":"15e55d77-dbbe-4b6c-80f5-9e91ed461be3","year":2017},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.243725Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:60ca12036737162f03a1043722a19b6dad47709963863b4ee89e3897c4e9ae4f","observation_id":"ac7f6e62-75f0-42ce-9c31-3881fcdc0007","resolution":{"observed_at":"2026-08-12T15:29:17.674101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.662817Z","title":"Deep learning for environmental conservation,","venue":null,"work_id":"259f934e-79af-46d1-b220-861d6da6eb65","year":2019},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.246586Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:dc619de42ec43745921c22134748a97f21d3f314e5e0e9f822f25ae14cdabecb","observation_id":"d6d90841-9d29-401a-b195-d09ed683a2e8","resolution":{"observed_at":"2026-08-12T15:29:17.665802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14458","last_updated":"2024-10-30T01:49:34Z","snapshot_observed_at":"2026-08-16T19:20:32.438778Z","submitted_at":"2024-05-23T11:44:29Z","title":"YOLOv10: Real-Time End-to-End Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14458","snapshot_observed_at":"2026-08-12T15:29:17.249401Z","title":"Yolov10: Real-time end-to-end object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.249401Z"},"links":{"cited_paper":"/paper/2405.14458","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:0ac6ffaa2c8f45113ec7f4216a207d6369d3062a7e59226d0bf8f029a4469502","observation_id":"d6b1e5d0-af1f-4b75-83ca-d77d0448a4ca","resolution":{"observed_at":"2026-08-12T15:29:17.249401Z","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":"2026-08-12T15:29:17.655359Z","title":"microsoft/Phi-3.5-vision-instruct,","venue":null,"work_id":"f108bba9-c529-4ddd-adbd-638ede54abb1","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.252753Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:6a508b3b49ee4730e25c01c047166eddfda0c945ae56ba8a5c8a59c368056fbf","observation_id":"77e1fcd6-4b57-4bdf-93b3-57aaf29d0e8a","resolution":{"observed_at":"2026-08-12T15:29:17.657780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.647879Z","title":"Attention is all you need,","venue":null,"work_id":"6d13e9f6-cbfb-469e-8367-56dfd44fd571","year":2017},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.256059Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:26133e6ec252856638f1bfc055cc1c662c30caa5325849a70de943128d566b29","observation_id":"30e7183b-d391-430c-8110-f8c9699da73b","resolution":{"observed_at":"2026-08-12T15:29:17.650524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.640295Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks,","venue":null,"work_id":"2be1ee13-59ff-45fa-8895-70faf6ae9768","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.259524Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:ed3ce4fff84fe201b2a3f1af0bfb591de084a8261fd375fe81f2d7677bd29cce","observation_id":"84bd4e60-6ca9-461b-a97a-54b1d220258f","resolution":{"observed_at":"2026-08-12T15:29:17.642970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.632777Z","title":"Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria: version 2.0,","venue":null,"work_id":"169ff3f7-4a4b-4e09-ab0c-8a5553c4257a","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.261876Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:5c7a659eb9e2f7c97c6586654c5e10934fa4e92402f952fa444090260d1b05d9","observation_id":"0c6b0f2c-f7f2-44da-bbf3-c70c1ec87d0a","resolution":{"observed_at":"2026-08-12T15:29:17.635419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.624946Z","title":"The LEDA Traitbase: a database of life -history traits of the Northwest European flora,","venue":null,"work_id":"81535587-9248-4564-9f68-305569dcac6d","year":2008},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.264204Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:388d35b8765c63b9d9b679ff5ab526aef101fbc6525b16b351c2f8f91ea633ae","observation_id":"1a899f03-6977-4e3b-968c-a580f33e97c6","resolution":{"observed_at":"2026-08-12T15:29:17.627649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.616618Z","title":"Open Science principles for accelerating trait -based science across the Tree of Life,","venue":null,"work_id":"d8bd0b4c-f071-4bdd-818a-c5574c29ac5c","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.266549Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:ee8e0000383f2101af8315bddbe30f98568de43eea0971188dfdb8ed1dd0618b","observation_id":"02aeebf4-cfeb-47d7-9b42-23eb5a804242","resolution":{"observed_at":"2026-08-12T15:29:17.619415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.608364Z","title":"Biocredits,","venue":null,"work_id":"e3f6d21e-8941-433d-bc99-5391f8b8dacc","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.268906Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:c1ced25e496e91e299fa9e51eb657245d3710686a37acffa829f321eb983b2d5","observation_id":"a2f6bf9d-22e0-4b4f-a744-9b4269b0e8c7","resolution":{"observed_at":"2026-08-12T15:29:17.611512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.598861Z","title":"Vision-language models for vision tasks: A survey,","venue":null,"work_id":"f11cd7fb-c299-42c7-91d3-ae7935117b4d","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.271408Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:95986959c027c680a60afd5259fc316f01540db4434bfbbae5572895969f0c3e","observation_id":"2c3854ed-7f36-4de8-ae6f-f12ac05e6978","resolution":{"observed_at":"2026-08-12T15:29:17.602426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.589914Z","title":"Real-time alerts from AI-enabled camera traps using the Iridium satellite network: A case-study in Gabon, Central Africa,","venue":null,"work_id":"ee681db7-3e56-46f7-a909-edd92b69967a","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.273912Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:d54494a8e1d3e9c1f53d7ff281c352788781273a461b01735431fbcfdb8f4ffb","observation_id":"4dac524f-f9df-4273-8c06-da8949943a10","resolution":{"observed_at":"2026-08-12T15:29:17.593120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.580908Z","title":"An evaluation of platforms for processing camera-trap data using artificial intelligence,","venue":null,"work_id":"2fa9256b-eab8-4589-b30f-a824c9531c1b","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.276460Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:5b32cfd24887d9b30c64359568d447c89c11353832a6ba909e88fa3634b29919","observation_id":"e4774155-ff8c-43e5-9db7-69cfc2c03083","resolution":{"observed_at":"2026-08-12T15:29:17.583560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.279092Z","title":"Fine-tuning llama for multi-stage text retrieval,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.279092Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:f935d85489f6e3fbcd29bcc42dab6f3e11de78e0316560b1a1e10c88ad6c92f3","observation_id":"455eab2f-5c95-465f-8b0d-e32a4d7e3092","resolution":{"observed_at":"2026-08-12T15:29:17.279092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08281","last_updated":"2025-10-23T09:36:08Z","snapshot_observed_at":"2026-07-31T05:45:37.385210Z","submitted_at":"2024-01-16T11:12:36Z","title":"The Faiss library","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08281","snapshot_observed_at":"2026-08-12T15:29:17.281463Z","title":"The faiss library,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.281463Z"},"links":{"cited_paper":"/paper/2401.08281","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:391b7a1f532c98f60589d532c687223a056671716243f9ef57b079301bdb0e23","observation_id":"1a03b37e-b83f-4790-8f1b-5861d6c63286","resolution":{"observed_at":"2026-08-12T15:29:17.281463Z","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":"2026-08-12T15:29:17.567579Z","title":"A survey on performance metrics for object -detection algorithms,","venue":null,"work_id":"fe71fb19-a18d-4b83-926a-067d0f403ce2","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.284313Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:a77808b7a29091f854e8e54e93e95fd02a6d0124c9750202dedb13a78c734431","observation_id":"20960549-e30c-4a9a-9c8f-1a97b9b9f067","resolution":{"observed_at":"2026-08-12T15:29:17.570511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.559758Z","title":"Faster R -CNN: Towards real -time object detection with region proposal networks,","venue":null,"work_id":"3a6fe36a-30f9-44ac-9978-d5085baff0ea","year":2016},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.286815Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:d502266626c267f1aeae47fd3704e0e162814b1ead0aec82029f8108086b53ce","observation_id":"ecf79a7d-2038-4020-b1c1-bd7c4598f954","resolution":{"observed_at":"2026-08-12T15:29:17.562414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.289582Z","title":"Deep learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.289582Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:755f28e29b4a6543a5c75f0f221fdfcba3cf0a1d5c81bc5a815607a2866b089f","observation_id":"6ea400c0-2a2b-4099-a005-686d8eefafb7","resolution":{"observed_at":"2026-08-12T15:29:17.289582Z","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":"2026-08-12T15:29:17.546420Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":"ec6f088a-1d80-44f0-b2c1-c58109f51358","year":2014},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.292339Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:311d40d8f6edd48e12dcb6b4bfc5545c70591e58dde145084800de39d79fcc73","observation_id":"c3de36a6-f465-404a-8b2e-5f1ba1288c87","resolution":{"observed_at":"2026-08-12T15:29:17.549071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.538823Z","title":"CSPNet: A new backbone that can enhance learning capability of CNN,","venue":null,"work_id":"18cc692c-5b8a-44ef-bdd0-c0c374cc4082","year":2020},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.295047Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:3b33b6d0bf712eac6c5a9b8ab95cc25cc6583fed2dd6ca650162b1ca6b64ff3e","observation_id":"77dff0e4-d1c3-46e7-92ee-f12bc32277b2","resolution":{"observed_at":"2026-08-12T15:29:17.541444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.531096Z","title":"Path aggregation network for instance segmentation,","venue":null,"work_id":"e0fdd937-3534-4498-bf80-6c0c6f1529cc","year":2018},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.297552Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:6052b1832b7ee5249a800165437e766458fce915f91096d0d3fd7d17999f407e","observation_id":"69ddbc1c-3545-40b7-83ea-c16d28f60dbf","resolution":{"observed_at":"2026-08-12T15:29:17.533582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.522605Z","title":"Learning non -maximum suppression,","venue":null,"work_id":"95e9d405-0425-4aa0-b6a0-46cd5bc91ecd","year":2017},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.300410Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:10a36cc9791b4024485b3d74f3292abd389f8223568c16e4426b71b195033a5f","observation_id":"62506e8a-2174-49b0-870e-07a66f825ff0","resolution":{"observed_at":"2026-08-12T15:29:17.525576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-12T15:29:17.302705Z","title":"Comprehensive Performance Evaluation of YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.302705Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:a408c9da3a83739758041ba802e54e397f934c9076b72cb5d9ac014b32c2fa8d","observation_id":"bb8891c3-888e-4c63-8814-b5da8e84bbd1","resolution":{"observed_at":"2026-08-12T15:29:17.302705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19407","last_updated":"2025-06-13T17:27:50Z","snapshot_observed_at":"2026-08-16T13:43:53.817788Z","submitted_at":"2024-06-12T06:41:23Z","title":"YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19407","snapshot_observed_at":"2026-08-12T15:29:17.305496Z","title":"Yolov10 to its genesis: A decadal and comprehensive review of the you only look once series,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.305496Z"},"links":{"cited_paper":"/paper/2406.19407","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:b6aac8a4cc1e17050773d428100cb01d8adcbafad130c8deeb120ddb0300d6e6","observation_id":"b2c40b85-8500-4139-b778-28b06a5658ff","resolution":{"observed_at":"2026-08-12T15:29:17.305496Z","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":"2026-08-12T15:29:17.513823Z","title":"Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server,","venue":null,"work_id":"19ba21b7-6586-40dc-bdff-aa5cf477deea","year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.308130Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:64e3f18d45e4f119f3cdeb004a0fdea499f20d216cfc71566fad8e0260a73bfb","observation_id":"1bd564e4-049b-47e5-8cd6-e219b397c914","resolution":{"observed_at":"2026-08-12T15:29:17.517089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14219","last_updated":"2024-08-30T21:17:17Z","snapshot_observed_at":"2026-08-17T03:25:04.404839Z","submitted_at":"2024-04-22T14:32:33Z","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14219","snapshot_observed_at":"2026-08-12T15:29:17.310573Z","title":"Phi-3 technical report: A highly capable language model locally on your phone,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.310573Z"},"links":{"cited_paper":"/paper/2404.14219","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:cb5af3e26e1dabbe84d0cd63f1f2d348bdbb5411e702fceec3dcf70b74a80812","observation_id":"702aa9c0-304a-4006-89df-607aef4c08c9","resolution":{"observed_at":"2026-08-12T15:29:17.310573Z","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":"2026-08-12T15:29:17.314280Z","title":"Selective kernel networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.314280Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:c83a38fe21012739abf2796447a5d335b04341af4e0e9bc4646ec423e726eb28","observation_id":"727d5701-22b9-4e91-9504-a9dd66fc4beb","resolution":{"observed_at":"2026-08-12T15:29:17.314280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02988","last_updated":"2024-07-03T10:40:20Z","snapshot_observed_at":"2026-08-19T12:36:35.757681Z","submitted_at":"2024-07-03T10:40:20Z","title":"YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02988","snapshot_observed_at":"2026-08-12T15:29:17.317511Z","title":"YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.317511Z"},"links":{"cited_paper":"/paper/2407.02988","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:96b600000799766fc8e5e4e6fccc9c1ba19f7cabbbd8f2eb8ddd4d365d6577ed","observation_id":"7a83c893-e2ce-42b0-a68d-9be21d5ae908","resolution":{"observed_at":"2026-08-12T15:29:17.317511Z","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":"2026-08-12T15:29:17.499228Z","title":"Creating large language model applications utilizing langchain: A primer on developing llm apps fast,","venue":null,"work_id":"887d1fab-aa53-40f5-b2eb-50104a238a2d","year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.320879Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:314e337bec7150689ecdfdf8c722b836e3cc32cecbcae1c5e1775096d6f8a836","observation_id":"4840f6fb-6803-499e-9d57-c95f8d4c9e89","resolution":{"observed_at":"2026-08-12T15:29:17.503139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08701","last_updated":"2024-02-13T18:37:25Z","snapshot_observed_at":"2026-08-19T15:08:34.393456Z","submitted_at":"2023-07-17T17:59:40Z","title":"AlpaGasus: Training A Better Alpaca with Fewer Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08701","snapshot_observed_at":"2026-08-12T15:29:17.323834Z","title":"Alpagasus: Training a better alpaca with fewer data,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.323834Z"},"links":{"cited_paper":"/paper/2307.08701","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:02516f2ccbf94b45db564f9ad79b231259da03db5b959ffb42cc0aa85c9a9a53","observation_id":"645ea9d3-7aee-4d05-b1a4-477a66c6b00f","resolution":{"observed_at":"2026-08-12T15:29:17.323834Z","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":"2026-08-12T15:29:17.489911Z","title":"Foundations of JSON schema,","venue":null,"work_id":"3d3c3c97-1118-40f8-8eaa-0d8a2be5fad1","year":2016},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.327074Z"},"links":{"citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:20b79605c5b2be2d4347d076a0005a73308ab27d35b995f1f1451a18d828149c","observation_id":"6add79c8-7be3-41c1-be4b-04b120b14232","resolution":{"observed_at":"2026-08-12T15:29:17.493520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09135","last_updated":"2024-04-14T03:54:00Z","snapshot_observed_at":"2026-08-16T14:01:09.360541Z","submitted_at":"2024-04-14T03:54:00Z","title":"Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09135","snapshot_observed_at":"2026-08-12T15:29:17.330034Z","title":"Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T15:29:17.330034Z"},"links":{"cited_paper":"/paper/2404.09135","citing_paper":"/paper/2411.14219"},"observation_digest":"sha256:3a6ed05a0ccb2a6d4fe042a085f3993e0329999285392e1f21fd38d58b68da9d","observation_id":"0ca8e3ae-cec8-4248-bedc-072b43c5fa62","resolution":{"observed_at":"2026-08-12T15:29:17.330034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.14219","last_updated":"2024-11-21T15:28:52Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T12:37:17.278309Z","submitted_at":"2024-11-21T15:28:52Z","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":47},"total_outbound_references":62},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2411.14219."}