{"as_of":"2026-08-22T22:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ddbd73c7d4e8d6858aa6fced7f2bea72fd8b988914839eafcb69e660bcb7cdf7","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:45:24.909810Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2505.00448/citation-record","integrity":"/paper/2505.00448/integrity","json":"/paper/2505.00448/citation-record.json","paper":"/paper/2505.00448"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.32614/cran.package.effectsize","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:45:24.998718Z","title":"Ben-Shachar, Dominique Makowski, Daniel Lüdecke, Indrajeet Patil, Brenton M","venue":null,"work_id":"3a49b6df-dc74-4ccc-b816-96d46561b1aa","year":2019},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.786521Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:ec86db6c5ab9a3214f8e7db7272c3d6359a0abd5e93cf80343f89ba810bf5f78","observation_id":"605e94de-56b7-4afc-a43b-092f6e5af5fb","resolution":{"observed_at":"2026-08-16T04:45:25.010340Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.371908Z","title":null,"venue":null,"work_id":"1059b09d-c399-43aa-8da0-ea915a9a1f16","year":1995},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.791402Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:ac75f8cfcc9bff8b92ea8396753886240b7182a858f4d7928784dcba75fa6b63","observation_id":"183e1ed5-4c34-477f-8646-e383a80e75cf","resolution":{"observed_at":"2026-08-16T04:45:25.376014Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.360024Z","title":null,"venue":null,"work_id":"5a423ab1-622f-4561-ba4f-f331b8b8095e","year":2001},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.796264Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:43ab85c0aff1e82b52e2b6383dd3f052c40f16e241ddfc87fb912e6f90bfe756","observation_id":"636719d0-9423-47cc-8382-ac096b23818f","resolution":{"observed_at":"2026-08-16T04:45:25.364106Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.347943Z","title":null,"venue":null,"work_id":"817e8b0d-e1b4-447a-99f2-f503a1a3e021","year":null},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.800336Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:2c9971cd9012cca9e47b73b0953d6ce4442d94e8c1020dbe96f77f1db3735493","observation_id":"a5079065-7d69-416e-bbb0-c88e89e632d8","resolution":{"observed_at":"2026-08-16T04:45:25.351836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.334995Z","title":null,"venue":null,"work_id":"4af3cee5-7e12-48be-9316-a640ed8ed1ab","year":null},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.804608Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:e481d4d0bcf821ded6a00ae26eefffcc0b07fd29f5159249f43b0ade98ac12d6","observation_id":"3f03a4c2-fb2b-4497-b038-c149793d8bf0","resolution":{"observed_at":"2026-08-16T04:45:25.339113Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.321669Z","title":null,"venue":null,"work_id":"55c9accc-3878-49ff-a931-4e8fc42c9b8b","year":1961},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.809377Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:c77112cdf3c490c101fb1df33a618cc9c30f589c01e1e7f791fa7d781aae50ab","observation_id":"72b1edea-04c6-4814-adb4-617fa153723a","resolution":{"observed_at":"2026-08-16T04:45:25.326110Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.308121Z","title":null,"venue":null,"work_id":"045eb8fb-2320-4c37-aeef-5212585465de","year":2012},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.813775Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:a5d5fb2f2caffd538b7d0e0ca9179d21d37e5c9fcc96b3185ee051e793fffb4b","observation_id":"02eb2a75-0b2f-4e7b-9f27-1d50797f2e7c","resolution":{"observed_at":"2026-08-16T04:45:25.312281Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.295542Z","title":null,"venue":null,"work_id":"4fac0997-ce50-46b2-b98f-cfeb2dcd1b20","year":2023},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.817992Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:b79ad487ead7169377d1305e53b080ba90f770b7c2ed5b41ded9c748b70c6575","observation_id":"474a9414-8940-49a3-91e2-e36f82bf1ae2","resolution":{"observed_at":"2026-08-16T04:45:25.300143Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.282492Z","title":null,"venue":null,"work_id":"836d08ab-5bba-458b-841c-51ffd4c5eb3d","year":2003},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.822682Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:7e7971e26e65c2d9a8c7bcfe25a7307b727f2df1a07a3376d019333bde7ce0eb","observation_id":"457503ce-67da-474c-bd63-fc252967a692","resolution":{"observed_at":"2026-08-16T04:45:25.286552Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.826804Z","title":"Harris, K","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.826804Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:d57fa3ae1e26f5abf8eb97c47f9df548c9d17a939ef58ea3cdb773a631d6ba0a","observation_id":"b196163a-ee10-4d38-aa1e-48e2a6d4c0b2","resolution":{"observed_at":"2026-08-16T04:45:24.826804Z","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":"2026-08-16T04:45:25.260166Z","title":"2017.pybind11 – Seamless operability between C++11 and Python","venue":null,"work_id":"14b99838-7159-4c75-82c8-30d088cf0098","year":2017},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.831195Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:7e79c7b92fde628b1779f47d3b482f13980aa637355ed3cfa60e15c5ef893cfc","observation_id":"a676585c-80fc-4c27-a51e-a106f95ae4ea","resolution":{"observed_at":"2026-08-16T04:45:25.264688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.32614/cran.package.rstatix","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:45:24.985875Z","title":null,"venue":null,"work_id":"4954343e-a9e4-4856-b17a-08b8e1e77d6f","year":2019},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.835024Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:19fb3d7149f5d94a6cfd6b9218b8af034a8e82413a7e6e9ed4fb209fdff8f28e","observation_id":"82b06ad0-dda0-41fa-b648-7e6fecbaed21","resolution":{"observed_at":"2026-08-16T04:45:24.990120Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.838966Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.838966Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:c890ff5b61b024c00393938c740a375f1fa3d6ed9a4a3aa2421440453914f809","observation_id":"9983773a-bb41-45f0-9c98-32a98cd7c550","resolution":{"observed_at":"2026-08-16T04:45:24.838966Z","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":"2026-08-16T04:45:25.245528Z","title":null,"venue":null,"work_id":"2b80eda6-09d8-4e7a-a111-311c22aa48d8","year":2024},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.842853Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:14f63f0eea459305c6ad5c09d6409484c85f683ce097d5ffdd51920c30bdd996","observation_id":"3ecbe603-f62a-42e2-b499-d6220d3df815","resolution":{"observed_at":"2026-08-16T04:45:25.249957Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.230337Z","title":"Blumenthal Weichung J Shih, Jay P Siegel, and Hal Stern","venue":null,"work_id":"1c72f6a6-9133-42b6-919a-0f50df8480a5","year":2012},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.846406Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:d1eb4ed59d83e3dc9f373794ea92356f0f4b2a4d6a11268303140622fe5edd3a","observation_id":"1b508816-c6d1-4605-96f5-0970f7f26f89","resolution":{"observed_at":"2026-08-16T04:45:25.236226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.213569Z","title":null,"venue":null,"work_id":"ae18b564-268a-4c93-9461-5d3524f337fb","year":null},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.850137Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:c633ae31a6ce238019042c8d92eb310c7968f51e6bdab7d3b47aea00f6489b04","observation_id":"eccbd552-de1d-4074-a0e7-82a2355b3da7","resolution":{"observed_at":"2026-08-16T04:45:25.217873Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.199149Z","title":null,"venue":null,"work_id":"5a80ac25-b26e-4984-af5b-06961880cbb7","year":null},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.854201Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:337717578ce5c82e600f2cb29dc39e25a65256061998d30cf0d0001e595419c1","observation_id":"f3f255f6-170b-41a3-b9f1-4c1eb92c16d4","resolution":{"observed_at":"2026-08-16T04:45:25.204134Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.182256Z","title":null,"venue":null,"work_id":"425817a4-718b-4070-ad97-b44b9ef830e6","year":2024},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.858401Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:119bded2cc7148a9c29ccf99315703ead06f67b9c2ce1147a0f9f48822060032","observation_id":"6538748d-d6d7-40d1-a062-20c6cd29c6c3","resolution":{"observed_at":"2026-08-16T04:45:25.187531Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.32614/cran.package.lsr","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:45:24.972140Z","title":"Learning Statistics with R","venue":null,"work_id":"a72891b2-cc35-43da-96c2-1927854bc706","year":2011},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.862274Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:504f00f1b82a3f86f3324d8653d0a76befd04193c68f410249ae79089075cc8b","observation_id":"81771f4b-e24b-4f42-bac4-5c5b1b7b3c7d","resolution":{"observed_at":"2026-08-16T04:45:24.977366Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.867986Z","title":"2024.pandas-dev/pandas: Pandas","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.867986Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:3c78cf88270b5ccb65c189b37bb1c84832ee73bcf76ca2e9662621da2e931b01","observation_id":"517c0227-5d36-46ab-80fd-f9f89015fa85","resolution":{"observed_at":"2026-08-16T04:45:24.867986Z","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":"2026-08-16T04:45:25.168380Z","title":null,"venue":null,"work_id":"1e837e85-0c4d-440c-be52-8fff81396a2c","year":2015},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.872493Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:7add62d029478771fca593d4bd5abb59302883a713d127f395c2080f927bd09b","observation_id":"5c10cb1e-efce-4232-8577-b43808795155","resolution":{"observed_at":"2026-08-16T04:45:25.172330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.877833Z","title":"2024.R: A Language and Environment for Statistical Computing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.877833Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:2a776396b9019875b3dbc26490df738de6b86a473339662f179a92e287ae19ea","observation_id":"232cccfe-01dd-4c08-802a-d725fc05a108","resolution":{"observed_at":"2026-08-16T04:45:24.877833Z","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":"2026-08-16T04:45:25.146338Z","title":null,"venue":null,"work_id":"9452c614-28fe-4274-8178-cafef77ff70b","year":2020},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.882637Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:a432f3d05476ad627f8c0aa5069451721e870b901c79c7e2fc9430df25c0938d","observation_id":"80ed6bf1-c532-4181-b917-d5157dea42ea","resolution":{"observed_at":"2026-08-16T04:45:25.150776Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.131453Z","title":null,"venue":null,"work_id":"0f86e504-f152-4542-9a9e-189a0e159005","year":2021},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.886984Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:5949884e0f6b584dd7d8e1f60848b35ec3d3f0063185df3b517ef37ce410ed47","observation_id":"abcf7498-82fc-4076-b73d-831db79a8ce5","resolution":{"observed_at":"2026-08-16T04:45:25.135430Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:25.118030Z","title":null,"venue":null,"work_id":"25e1f515-67d2-487f-82ee-3df9ba868927","year":2023},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.892034Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:56d33b24ecdc9a246358d5f657b02f3ee23a4af00dd95e3e246b9088db257734","observation_id":"96577d14-7b7a-4749-aed3-bb40cd6223ec","resolution":{"observed_at":"2026-08-16T04:45:25.122665Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.896785Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.896785Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:b4211360fff1447aa09f2ad83b54df8f55f258afd59843c86e2477642f53f2c6","observation_id":"d02e1bce-4a95-4568-aee2-2f1e9af042b5","resolution":{"observed_at":"2026-08-16T04:45:24.896785Z","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":"2026-08-16T04:45:24.900760Z","title":"Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.900760Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:ecdb686e583c9a6114eb1defee9ea81a395ffd41942edf3eac9c82c409ab4a58","observation_id":"56a9b83d-bfea-4119-a3ca-0fd7ff93fea5","resolution":{"observed_at":"2026-08-16T04:45:24.900760Z","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":"2026-08-16T04:45:25.095723Z","title":null,"venue":null,"work_id":"d927edba-1edd-4bd1-a4ec-48fb521bd069","year":2011},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.904992Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:cc35f8bfd969735940368aee4242e512528119e386c79d13cd819b2129629393","observation_id":"58b6605e-ea70-41c6-bd5b-e65a2d0041a3","resolution":{"observed_at":"2026-08-16T04:45:25.100569Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-16T04:45:24.909810Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:45:24.909810Z"},"links":{"citing_paper":"/paper/2505.00448"},"observation_digest":"sha256:d7efd5abf2edb8f91c3875f89ff51214a29e832fd9467b2174144d492deba9a4","observation_id":"8327654a-f000-48a7-9010-921f56f5f285","resolution":{"observed_at":"2026-08-16T04:45:24.909810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.00448","last_updated":"2025-05-01T10:45:37Z","latest_version":1,"primary_category":"cs.MS","snapshot_observed_at":"2026-08-16T04:39:45.268852Z","submitted_at":"2025-05-01T10:45:37Z","title":"NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":5,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":29},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2505.00448."}