{"as_of":"2026-08-09T00:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b656fc746fd49849eaa0e90750d49d8e895f3b4eff12cb60d857190ff72b7feb","coverage":[{"denominator":10,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:29:34.923297Z","state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2505.14964/citation-record","integrity":"/paper/2505.14964/integrity","json":"/paper/2505.14964/citation-record.json","paper":"/paper/2505.14964"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:36.507760Z","title":"Unbiggen AI","venue":null,"work_id":"68515efd-abad-4b4e-ac91-07bf9fc3087f","year":2021},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.111638Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:13a6bbc1cce3e46e36e49a28dff2d1abcb6882ef6eeee223b7f11cd4d871cec3","observation_id":"d60f03ae-04af-4249-90f1-2c87769105cb","resolution":{"observed_at":"2026-08-07T15:29:36.606901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T15:29:36.321756Z","title":"ML Models on a Data Diet","venue":null,"work_id":"ae743b54-88ee-4e5f-a62b-ef5874549058","year":2023},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.204515Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:941221140f5f0e0ff18f839b65cd375d59fa41bd2ba08e36cc7f25b796228263","observation_id":"d0b727a9-2831-4615-9416-ded49d93a0a7","resolution":{"observed_at":"2026-08-07T15:29:36.409088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T15:29:34.270279Z","title":"Data Cascades in High-Stakes AI","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.270279Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:3a6ca4ae9151e048bb28882fbe93167a3d32f56ea814c3f0294b5cf5e85186cc","observation_id":"7736a094-a454-47ac-8558-7b6832e8de38","resolution":{"observed_at":"2026-08-07T15:29:34.270279Z","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":"2026-08-07T15:29:36.133324Z","title":"Target-Aware Active Learning (TAAL)","venue":null,"work_id":"b30ca5f7-bdea-4b8b-af53-52f352ba89b9","year":2023},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.352171Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:899a10030b5b291b561f0c7188afa1459c5a31ed4c2de1facd2084cedc2981c8","observation_id":"a8dc7303-8e0a-42a3-86ea-d83e2be07cfd","resolution":{"observed_at":"2026-08-07T15:29:36.214065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T15:29:35.954026Z","title":"Annotation-Free Object Detection by Knowledge-Extraction from Visual-Language Models","venue":null,"work_id":"4cb606ab-dcd3-47a8-979f-54bf0bb5ba6c","year":2025},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.429908Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:5cdf13914f56becb4a6152db038655c4a821212c02167bc57e78228f8497aa02","observation_id":"70645adf-9619-4a0f-ba06-28ac24df18ea","resolution":{"observed_at":"2026-08-07T15:29:36.039896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/electronics14081651:contentreference","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:35.032772Z","title":"F2SOD: A Federated Few-Shot Object Detection","venue":null,"work_id":"b6fe4bb7-f8ca-4572-a830-c47f9a843618","year":2025},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.561353Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:20a5a5b7af6f20936b5019fa7ebad5056b3832f7e627f7b75724a21c09be8a20","observation_id":"d5ba82c1-56f1-4747-97a6-4b9f16423c79","resolution":{"observed_at":"2026-08-07T15:29:35.126848Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T15:29:35.773813Z","title":"Human-Machine Collaboration on Image Annotation","venue":null,"work_id":"8007a33e-17db-4b58-ad99-e64aa9169066","year":2021},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.632178Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:aef85191e9f40b49d2f2de6b20123b195ca1179be7a4357ef5018b041b2dce0e","observation_id":"bf68a1b8-0251-4ad2-876a-dcfdb4e37497","resolution":{"observed_at":"2026-08-07T15:29:35.848086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.19088","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:35.298990Z","title":"A Survey of Image Labelling for Computer Vision Applications","venue":null,"work_id":"4a30ecb3-ed8c-43c5-8908-ca088091c394","year":2021},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.820823Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:4b175ee326c12c2b8cbb5a2dbe1a495384553ce5d39cb0703ccf29e7300066cd","observation_id":"e47e4211-58e7-4eb7-9627-ca2db12c962a","resolution":{"observed_at":"2026-08-07T15:29:35.385227Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.14749","last_updated":"2021-11-07T13:04:04Z","snapshot_observed_at":"2026-08-04T22:08:50.379039Z","submitted_at":"2021-03-26T21:54:36Z","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.14749","snapshot_observed_at":"2026-08-07T15:29:34.923297Z","title":"Pervasive Label Errors in Test Sets Destabilize Benchmarks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.923297Z"},"links":{"cited_paper":"/paper/2103.14749","citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:e79b3f503ae3cb104d3983d28945a6a36911345933cd2ce4560ff315464029c4","observation_id":"6564c559-b69d-4101-bf50-bc41bfde0b53","resolution":{"observed_at":"2026-08-07T15:29:34.923297Z","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":"3856.34739","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:35.547744Z","title":null,"venue":null,"work_id":"2bdf5682-4018-4630-9f34-6233a3157d64","year":null},"citing_paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T15:29:34.743551Z"},"links":{"citing_paper":"/paper/2505.14964"},"observation_digest":"sha256:f8964eb5df885c730afa0ccf576cd0a846859c05ef08696c77a9f1eabe929a12","observation_id":"a5f4a182-6ade-4255-8d81-8aa3b5496901","resolution":{"observed_at":"2026-08-07T15:29:35.597354Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.14964","last_updated":"2025-05-20T22:57:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:24:13.333679Z","submitted_at":"2025-05-20T22:57:35Z","title":"The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models"},"reference_resolution":{"displayed":10,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":10},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2505.14964."}