{"as_of":"2026-08-18T09:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0fc114c1ab4bfdfbd491f8a2d7ba6188d264fa18aa2457074c7f9c25b6a65180","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:47:22.473368Z","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":"2026-05-19T08:27:11.235435Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11657","snapshot_observed_at":"2026-08-16T05:47:22.473368Z","title":"Landreman, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19861","last_updated":"2025-04-28T14:51:55Z","snapshot_observed_at":"2026-08-16T17:51:47.755427Z","submitted_at":"2025-04-28T14:51:55Z","title":"HIPED: Machine Learning Framework for Spherical Tokamak Pedestal Prediction and Optimization","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-16T05:47:22.473368Z"},"links":{"cited_paper":"/paper/2502.11657","citing_paper":"/paper/2504.19861"},"observation_digest":"sha256:3c854def3fb730db609f231d9a9e9c7b6e2d63051d3095f2ee566d7e0dc1e245","observation_id":"32bdd970-ba4b-4015-a853-01ebee83ed82","resolution":{"observed_at":"2026-08-16T05:47:22.473368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11657","snapshot_observed_at":"2026-08-16T00:48:42.129116Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? insights from data and machine learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.02727","last_updated":"2025-05-05T15:27:28Z","snapshot_observed_at":"2026-08-16T00:40:22.301569Z","submitted_at":"2025-05-05T15:27:28Z","title":"Prediction of ELM-free Operation in Spherical Tokamaks With High Plasma Squareness","version":1},"reference_index":205,"source":"pdf_text","source_observed_at":"2026-08-16T00:48:42.129116Z"},"links":{"cited_paper":"/paper/2502.11657","citing_paper":"/paper/2505.02727"},"observation_digest":"sha256:1afb61f24c854bcd9548b28cb38c911a67dae2d94f3b3bbbfb4d4f33572d6f0d","observation_id":"bc6f5634-f3b0-4965-a4ac-824aa7c9ee32","resolution":{"observed_at":"2026-08-16T00:48:42.129116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning","version":2},"cited_work":{"arxiv_id":"2502.11657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.11657","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"H E & Plunk, G","venue":null,"work_id":"53bc824b-d533-4ba9-a291-d523481c52d4","year":2013},"citing_paper":{"arxiv_id":"2506.22166","last_updated":"2026-04-30T06:08:08Z","snapshot_observed_at":"2026-08-02T10:20:38.947104Z","submitted_at":"2025-06-27T12:27:13Z","title":"Critical gradient optimization for quasi-isodynamic stellarators","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T08:26:22.124356Z"},"links":{"cited_paper":"/paper/2502.11657","citing_paper":"/paper/2506.22166"},"observation_digest":"sha256:9c8444e268b6f5288fd475549591843bf3e97a0b4ded4adc239adbccd830e975","observation_id":"e27c1946-b057-46da-8714-599ea076e5ca","resolution":{"observed_at":"2026-05-19T08:27:11.238547Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11657","snapshot_observed_at":"2026-08-06T20:10:02.646244Z","title":"Landreman, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.03776","last_updated":"2025-07-04T18:54:02Z","snapshot_observed_at":"2026-08-06T20:00:29.927897Z","submitted_at":"2025-07-04T18:54:02Z","title":"Data-Driven Approach to Model the Influence of Magnetic Geometry in the Confinement of Fusion Devices","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:10:02.646244Z"},"links":{"cited_paper":"/paper/2502.11657","citing_paper":"/paper/2507.03776"},"observation_digest":"sha256:9a8e3fa52a1f0829a6e6eb4ae4d7518b4d5222eefb9be71514277c42bb4c3bbe","observation_id":"36a89670-095f-4143-81f0-3fe5d4e232f4","resolution":{"observed_at":"2026-08-06T20:10:02.646244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11657","snapshot_observed_at":"2026-08-06T16:34:44.774150Z","title":"Landreman, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.13144","last_updated":"2025-07-17T14:08:36Z","snapshot_observed_at":"2026-08-13T02:08:35.019547Z","submitted_at":"2025-07-17T14:08:36Z","title":"Introduction to Stability and Turbulent Transport in Magnetic Confinement Fusion Plasmas","version":1},"reference_index":235,"source":"pdf_text","source_observed_at":"2026-08-06T16:34:44.774150Z"},"links":{"cited_paper":"/paper/2502.11657","citing_paper":"/paper/2507.13144"},"observation_digest":"sha256:faa2d9e6ccf01f9850b9707cd7817b1125f2e434bb340b4595ae15e6e6ae161a","observation_id":"2ca5a7a0-8314-4afd-a335-42084ec16076","resolution":{"observed_at":"2026-08-06T16:34:44.774150Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.11657/citation-record","integrity":"/paper/2502.11657/integrity","json":"/paper/2502.11657/citation-record.json","paper":"/paper/2502.11657"},"outbound":[],"paper":{"arxiv_id":"2502.11657","last_updated":"2025-06-03T13:56:01Z","latest_version":2,"primary_category":"physics.plasm-ph","snapshot_observed_at":"2026-08-18T09:11:22.214836Z","submitted_at":"2025-02-17T10:48:26Z","title":"How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.11657."}