{"as_of":"2026-08-10T18:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:68250b6609f1578d5ab4f83724b37bb40245238e951aacba10b69cb1746dddef","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:20:29.447891Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-17T22:12:05.816125Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":"1903.03096","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","venue":null,"work_id":"91703c4e-080b-44e9-8bf9-d8dbe40ec3a2","year":1903},"citing_paper":{"arxiv_id":"1910.04867","last_updated":"2020-02-21T13:36:15Z","snapshot_observed_at":"2026-08-09T06:48:42.729935Z","submitted_at":"2019-10-01T17:06:29Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T22:12:05.731960Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/1910.04867"},"observation_digest":"sha256:95c23cb51af0895e6a4c84d9ba6207df617d758cce2b41111c8ac94d218e9980","observation_id":"a0f96071-7c24-4e8f-9796-a08351c3fafa","resolution":{"observed_at":"2026-05-17T22:12:05.818527Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-08-07T15:20:29.447891Z","title":"Meta-dataset: A dataset of datasets for learning to learn from few examples.arXiv preprint arXiv:1903.03096,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2505.15506","last_updated":"2025-05-21T13:26:56Z","snapshot_observed_at":"2026-08-08T03:22:29.275022Z","submitted_at":"2025-05-21T13:26:56Z","title":"Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:20:29.447891Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2505.15506"},"observation_digest":"sha256:841b818cf0d0e7c25801735a0aa7a1b13c83ab513d638e21034ef392fd1d1749","observation_id":"18f29162-7540-4334-bff4-2204242fedb7","resolution":{"observed_at":"2026-08-07T15:20:29.447891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-08-06T18:06:32.713704Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.09299","last_updated":"2025-07-12T14:19:04Z","snapshot_observed_at":"2026-08-09T11:58:28.732393Z","submitted_at":"2025-07-12T14:19:04Z","title":"ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:06:32.713704Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2507.09299"},"observation_digest":"sha256:0b0cfc528220d67c33c27ff9b152b7bd128f737a747740b5e3ca4ac2ef0d220b","observation_id":"f201ead1-cb04-43e5-9fa5-d5e1e4782f7f","resolution":{"observed_at":"2026-08-06T18:06:32.713704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-08-04T16:57:32.066879Z","title":"arXiv preprint arXiv:1903.03096","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2509.11219","last_updated":"2025-09-14T11:35:14Z","snapshot_observed_at":"2026-08-08T12:31:12.617952Z","submitted_at":"2025-09-14T11:35:14Z","title":"CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T16:57:32.066879Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2509.11219"},"observation_digest":"sha256:e2de87cf5a57ebe9f0c724cdf63e50a5d4ffdc7f7896464beb40919a3cc73c5b","observation_id":"dd6d0169-24be-4afa-bfa6-5b3d7d028131","resolution":{"observed_at":"2026-08-04T16:57:32.066879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":"1903.03096","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","venue":null,"work_id":"91703c4e-080b-44e9-8bf9-d8dbe40ec3a2","year":1903},"citing_paper":{"arxiv_id":"2602.19837","last_updated":"2026-05-06T07:57:45Z","snapshot_observed_at":"2026-07-06T22:46:45.213871Z","submitted_at":"2026-02-23T13:39:58Z","title":"Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent","version":3},"reference_index":171,"source":"pdf_text","source_observed_at":"2026-05-15T20:46:15.275441Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2602.19837"},"observation_digest":"sha256:c95466cc3ee2f77b6c2dd9aa2ce3dac3a42008951d0c6925b5832e70cddbac72","observation_id":"e4701cf0-7edb-4624-847a-a47cf8365a5d","resolution":{"observed_at":"2026-05-15T20:46:35.644472Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":"1903.03096","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","venue":null,"work_id":"91703c4e-080b-44e9-8bf9-d8dbe40ec3a2","year":1903},"citing_paper":{"arxiv_id":"2604.17569","last_updated":"2026-04-19T18:20:05Z","snapshot_observed_at":"2026-07-30T05:57:10.385626Z","submitted_at":"2026-04-19T18:20:05Z","title":"MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-10T05:58:17.974995Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2604.17569"},"observation_digest":"sha256:a4a46ae86f2171246e066d0b2bb50c25d7d3ca23e4afc2660bf1a593a0987ced","observation_id":"5f84cadf-33dd-4fa7-9037-e4b507922c73","resolution":{"observed_at":"2026-05-10T06:01:13.429246Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03096","snapshot_observed_at":"2026-08-02T07:40:22.094772Z","title":"arXiv preprint arXiv:1903.03096 , year=","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2607.20532","last_updated":"2026-07-10T13:07:12Z","snapshot_observed_at":"2026-08-08T11:53:10.019905Z","submitted_at":"2026-07-10T13:07:12Z","title":"Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-02T07:40:22.094772Z"},"links":{"cited_paper":"/paper/1903.03096","citing_paper":"/paper/2607.20532"},"observation_digest":"sha256:7acef8b423a09c2d6ee87a5ee6b699500fa9bad15b0f1c84446feaac90528ef7","observation_id":"01ef6343-61ae-46fe-ba82-2fc576fe97c2","resolution":{"observed_at":"2026-08-02T07:40:22.094772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1903.03096/citation-record","integrity":"/paper/1903.03096/integrity","json":"/paper/1903.03096/citation-record.json","paper":"/paper/1903.03096"},"outbound":[],"paper":{"arxiv_id":"1903.03096","last_updated":"2020-04-08T15:58:20Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T17:56:24.389759Z","submitted_at":"2019-03-07T18:48:55Z","title":"Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1903.03096."}