{"as_of":"2026-08-12T18:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e7285d705601d23ec8935143f61d5d51a204634dfc1a7f0b7782a3cc9a26a0df","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:56:23.319701Z","state":"measured"},{"denominator":70,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":70,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:23:51.556245Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T13:23:52.485841Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"cited_work":{"arxiv_id":"2501.03383","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.03383","snapshot_observed_at":"2026-08-06T13:23:52.485841Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","venue":"physics.comp-ph","work_id":"fbc2b5c9-f0cb-4d3c-8094-0bba79bbed49","year":2025},"citing_paper":{"arxiv_id":"2507.20719","last_updated":"2025-07-28T11:15:47Z","snapshot_observed_at":"2026-08-09T09:15:40.532821Z","submitted_at":"2025-07-28T11:15:47Z","title":"Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T13:23:51.556245Z"},"links":{"cited_paper":"/paper/2501.03383","citing_paper":"/paper/2507.20719"},"observation_digest":"sha256:0f57484fe651dc0fd1dd52184ad85b341ffd691147fa4817245173dcce8a07ac","observation_id":"477a18ab-c9e6-43d8-aee0-60549cf23ded","resolution":{"observed_at":"2026-08-06T13:23:52.598524Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.03383/citation-record","integrity":"/paper/2501.03383/integrity","json":"/paper/2501.03383/citation-record.json","paper":"/paper/2501.03383"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:56:24.410306Z","title":"AI-Augmented Facilities: Bridging Experiment and Simulation with ML (Dagstuhl Seminar 23132),","venue":null,"work_id":"de4904b7-b026-4112-b106-1ecb83c7b42f","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.007329Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:74581901e51ec0233213514b85828282c7f60fff81b897a84860ac61dcd75f55","observation_id":"054e3e38-3249-4437-b4d8-28518022c05c","resolution":{"observed_at":"2026-08-10T21:56:24.414240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.397484Z","title":"Deep learning for digital holography: a review,","venue":null,"work_id":"8aa93804-bf90-440f-97d7-4be1cd796884","year":2021},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.012110Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:6cf5a7b8b836a04b9e07bef88247d04a8273ba71093eca80eb7324d69fff7548","observation_id":"91bad78a-96a5-4b8c-b585-8125497fd790","resolution":{"observed_at":"2026-08-10T21:56:24.402581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.384582Z","title":"Neural Network Potentials: A Concise Overview of Methods,","venue":null,"work_id":"70b84493-37e1-4029-bc89-15514c23ca43","year":2022},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.016193Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e268f6f8b859265dc3545503731ea52de3fa00b703bc5c7589b606cd21786d62","observation_id":"392ccc29-62f0-4532-a1bb-5e868ae4f38a","resolution":{"observed_at":"2026-08-10T21:56:24.389003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.371374Z","title":"Data-driven Science and Machine Learning Methods in Laser-Plasma Physics,","venue":null,"work_id":"b7a5e8e3-032b-492c-addc-ca815567b367","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.020378Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:59eb413fec3adf200fa3e7f012634459be05da6309d0b2fa2e95e9cc2b074859","observation_id":"e84464f5-1c39-4b13-8929-c7f9ae27f8ba","resolution":{"observed_at":"2026-08-10T21:56:24.375856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.358213Z","title":"Machine learning for anomaly detection in particle physics,","venue":null,"work_id":"fd106c6b-3603-471b-8adc-a37c7df43e00","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.024896Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:f1bbee2b09bec265d6d4f9febfb254c01c1d01767c602bd1b9ea56bb55debfdd","observation_id":"b75c7c07-24d7-48fc-bd51-15569974eea9","resolution":{"observed_at":"2026-08-10T21:56:24.363508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.345030Z","title":"Radiative signatures of the relativistic Kelvin-Helmholtz instability,","venue":null,"work_id":"19bc0238-d3fc-4ff1-ae20-4737cc44e315","year":2013},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.028925Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:b45233e05d0708aaadb317fb4556658e12ac439343566931f46e4884449a1c4e","observation_id":"b109ec05-1dbb-4f93-8f05-49a30408d336","resolution":{"observed_at":"2026-08-10T21:56:24.349322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.332632Z","title":"Hardware-agnostic interactive ex- ascale in situ visualization of particle-in-cell simulations,","venue":null,"work_id":"09469ea9-f09a-4c8c-9b39-6252d5743cd3","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.033286Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:776e41c6f59bd10d844612fc9e5bec10eb89dc2c7e474eec2b6f5630a03c6699","observation_id":"8d639bf5-5ee9-4c22-a424-804789df2b23","resolution":{"observed_at":"2026-08-10T21:56:24.337305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.320767Z","title":"Stability of Hydromagnetic Kelvin-Helmholtz Discontinu- ity,","venue":null,"work_id":"6c98577e-f025-43eb-96d0-f90b1c0bdd4a","year":1963},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.037339Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:f03ca538cec737644aa5ceed57e48441d4f186a25e7dbf8bbc88e74163cf4804","observation_id":"cf065c59-7390-498e-a0a3-c02b1a7773d8","resolution":{"observed_at":"2026-08-10T21:56:24.324911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.041186Z","title":"Goodfellow, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.041186Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:d805b65ac3f183ec0cce94ce666cd16a873059b07931ec825c4fb01fe21a2da6","observation_id":"add7c847-9185-4452-84af-17d9dd3f3c70","resolution":{"observed_at":"2026-08-10T21:56:23.041186Z","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":"2026-08-10T21:56:24.290232Z","title":"Machine learning for streaming data: state of the art, challenges, and opportunities,","venue":null,"work_id":"357606c1-279c-4ec2-bdf9-932745adff36","year":2019},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.048714Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:539b70efc64460b50afecfb486503b93a90f15017c4bead40f284b9ca7ca7a80","observation_id":"971e8189-eef6-4f3b-bc4b-5c6b56660f3a","resolution":{"observed_at":"2026-08-10T21:56:24.294421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.276416Z","title":"A Comprehensive Survey of Continual Learning: Theory, Method and Application,","venue":null,"work_id":"0663892e-fe1f-4b26-abd0-92492ff2cdd5","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.052442Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:f6e3beb9bc090165174c4a04c27a88821bd9cdd7ac8ea2a1e2180a8a8d1cb511","observation_id":"5c25a724-1679-4b6a-960c-4bc49ccd2572","resolution":{"observed_at":"2026-08-10T21:56:24.280721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.264069Z","title":"On the scalability of data reduction techniques in current and upcoming hpc systems from an application perspective,","venue":null,"work_id":"15140f07-cb3a-40fb-9ff9-46855d61faee","year":2017},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.056424Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:6ead6c1f0f4ecb5bc60eb58025d81a294a6d222d14388e65b91b3362c748b6d1","observation_id":"e114bed0-ef20-458e-b7cb-708c4aa4efea","resolution":{"observed_at":"2026-08-10T21:56:24.268271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.251888Z","title":"Improving i/o performance for exascale appli- cations through online data layout reorganization,","venue":null,"work_id":"605c5646-d259-47be-9388-d28f1ec7c966","year":2022},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.060328Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:ba118e0c3138151d7f9a8f6890efd1c3f91c8d81e9e434cd6775bdf0678132c5","observation_id":"01f319b0-e4b1-4c3f-8e50-729767c253e2","resolution":{"observed_at":"2026-08-10T21:56:24.256431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.239274Z","title":"Big data multimedia mining: feature extraction facing volume, velocity, and variety,","venue":null,"work_id":"e5b3d371-a9fa-4d58-85ab-cc449e140eb1","year":2019},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.064328Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:195e3ea3e59f5e724e7d8b6c0da800f1148b8cb1d69fae0b2b3654b39fc49511","observation_id":"ce36731e-675f-4abf-baa9-56f22e07a54b","resolution":{"observed_at":"2026-08-10T21:56:24.243850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.226850Z","title":"Transitioning from file-based hpc workflows to streaming data pipelines with openpmd and adios2,","venue":null,"work_id":"c2a496c2-a4ff-488c-9689-3ff89b2d2507","year":2022},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.068175Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:9ea456bcc95afd032a960dfecb2811b9e459d4255e597320c5ca12a61969712e","observation_id":"8297a1a8-0254-43da-8cd3-6999dfa51603","resolution":{"observed_at":"2026-08-10T21:56:24.231066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.214875Z","title":"Frontier","venue":null,"work_id":"f0d1d378-a13d-41ec-a374-e2c9de027b85","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.071873Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e5e527434a9d57c2a4b75aa52bf8f6c7df51be1b43814f7c277b9665ef571d04","observation_id":"a3aea95a-3f7a-4e1c-9599-ca9acfa3dac2","resolution":{"observed_at":"2026-08-10T21:56:24.218821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.198755Z","title":"PUNCH4NFDI Use case class 5: real-time challenges, data irreversibil- ity,","venue":null,"work_id":"875e306a-a2bf-4975-b7b5-9ec2b01adffb","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.075557Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:aea415a67654b100470f3178596ebd9254f09563428b46af0c1d30010aefefd3","observation_id":"82ab1d65-ecbc-4e29-be72-f86e6f75aaba","resolution":{"observed_at":"2026-08-10T21:56:24.203815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.184751Z","title":"PyTorch Distributed Data Parallel Documentaion","venue":null,"work_id":"646dd968-39c3-46a7-a24e-51823e445d7c","year":null},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.079811Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:d114aaf7d6d2a972fc589c91791da140aefdd71384c1d4783bc38dd3c6f078a7","observation_id":"7862dbd3-4de0-4150-aed6-6903f9aa9153","resolution":{"observed_at":"2026-08-10T21:56:24.189236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.170393Z","title":"Kelvin-helmholtz instability at saturn’s magnetopause: Hybrid simulations,","venue":null,"work_id":"f3d06af9-eb2f-4fe0-841e-ee1369fbed87","year":2011},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.084682Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:c3397cd52586d3a02c37952f5dba3eeab81d478e092536d8bd1c6eee8b380b1a","observation_id":"aa54aaa4-5f70-415b-862e-c36876d87824","resolution":{"observed_at":"2026-08-10T21:56:24.174800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.158141Z","title":"Recent progress in quantifying hydrodynamics instabil- ities and turbulence in inertial confinement fusion and high-energy- density experiments,","venue":null,"work_id":"c1713638-bdbb-4704-9ed1-a8f8f8fe4600","year":2021},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.090915Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:5197056525eaad12d37e42e47198468a072dee4cb7fb0dd66912e3b3056a8227","observation_id":"d85f0310-f550-4090-ad59-79582f81406a","resolution":{"observed_at":"2026-08-10T21:56:24.162507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.145230Z","title":"Faster ablative kelvin–helmholtz instability growth in a magnetic field,","venue":null,"work_id":"8fbdffe3-6419-410a-8c80-9809e0225c55","year":2022},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.095009Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:7b739469ea9205514682c2ab0bc588117a51f0941fe5a9164cd06d239a4dc5ee","observation_id":"53efd6af-510e-4852-b60f-809380e5da5c","resolution":{"observed_at":"2026-08-10T21:56:24.149782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.132758Z","title":"dc-magnetic- field generation in unmagnetized shear flows,","venue":null,"work_id":"d817e9e5-ca21-43d1-92dc-c94512aece0c","year":2013},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.099331Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:6a006fcf377be145ee6f12e62a01a199689a46955ab0fdd6c10fce508702b35f","observation_id":"75295ba8-592f-46c5-910c-81fb700a8ec5","resolution":{"observed_at":"2026-08-10T21:56:24.136983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.120607Z","title":"Large-Scale Magnetic Field Generation Via the Kinetic Kelvin-Helmholtz Instability in Unmagnetized Scenarios,","venue":null,"work_id":"b4421480-79b0-449d-a0de-3109952aa947","year":2012},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.103397Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:635d03d67c2345fbc03f57d945805f2e0d08f4793e9aa3dd9d50fa360bc92300","observation_id":"31afa0fe-3e75-497e-9185-0d3a569b22aa","resolution":{"observed_at":"2026-08-10T21:56:24.125218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.108412Z","title":"Identifying the linear phase of the relativistic kelvin-helmholtz instability and measuring its growth rate via radiation,","venue":null,"work_id":"dc2bf054-3701-40fb-a1b3-098d7f123827","year":2017},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.107723Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:4c38f62bebdf94a7647af0d3cde95112e94b7f45719fe874778b4afe5662da53","observation_id":"f98f0547-d89a-4d80-a809-dc036d5e6e91","resolution":{"observed_at":"2026-08-10T21:56:24.112686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.096391Z","title":"PICon- GPU: A fully relativistic particle-in-cell code for a GPU cluster,","venue":null,"work_id":"0fd60fba-bf78-48ef-846b-9b28cf8fb83e","year":2010},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.112895Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:658ea8633a017b0e1d5304955885852569791913c4b764a15c24e49bab4170cc","observation_id":"567d7313-c5eb-46c8-b8ff-a0227c432e83","resolution":{"observed_at":"2026-08-10T21:56:24.100428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.083499Z","title":"Alpaka – an abstraction library for parallel kernel acceleration,","venue":null,"work_id":"c1737b23-1553-439f-96ac-83a7e7ebeb92","year":2016},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.116995Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:6ddf3f30f4162def3e91a04732106def36e50314e9adbd3a39a9fca479bee9b2","observation_id":"c37f4f92-9e0e-4c20-9b3a-b7770cdca32e","resolution":{"observed_at":"2026-08-10T21:56:24.088310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.072461Z","title":null,"venue":null,"work_id":"e436ecf0-9759-4238-9922-ecf4e27c8b9b","year":1988},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.121164Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:b9042245b6cb7933e1edc6c792d98dbd06908b254e67f24bc136a0d39b4d0213","observation_id":"781a8c8f-0f4f-409d-80c6-2748e15a4729","resolution":{"observed_at":"2026-08-10T21:56:24.076456Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.061451Z","title":null,"venue":null,"work_id":"e49462a4-f454-4b7c-8169-c69da0a3f626","year":2011},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.125856Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:11a32527c704ae7bf214f03a80ead161c27f9bd706fece8eb6a28490546a8dc7","observation_id":"d6471114-6be3-41c4-bb85-37ec32681836","resolution":{"observed_at":"2026-08-10T21:56:24.065122Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.050339Z","title":"Frontier User Guide: System Overview","venue":null,"work_id":"da6e28da-edf8-4e3b-9f58-5038f8c4e6a0","year":null},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.130093Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:c461aafc0a0dfb3586376f4cdec336a3c0e3dcfe2e1f3cf860986f8bb83acb1d","observation_id":"ac2cdd6e-d55f-4e1e-8789-2dbe7ea4296a","resolution":{"observed_at":"2026-08-10T21:56:24.054634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.037960Z","title":"TWEAC test-case PIConGPU","venue":null,"work_id":"1f75854c-5da2-4a0c-8cb7-85d416acb337","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.134836Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:62430f4d13c7b2ce0f592cf08eb07275714195c658d8b2819ceccfdaf6289727","observation_id":"6338539f-9efe-40fd-b1aa-570c56140da7","resolution":{"observed_at":"2026-08-10T21:56:24.041959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.026200Z","title":"Ready for the frontier: Preparing applications for the world’s first exascale system,","venue":null,"work_id":"55bf9182-2a73-469f-904d-f0ffccabd3d8","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.139029Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:c3f790d59047a49186af2bc562939d304c80962fcb8fe9854c38b40c6be220a4","observation_id":"32bda668-d95b-4a69-b4e4-27ce10455673","resolution":{"observed_at":"2026-08-10T21:56:24.030224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:24.009725Z","title":"A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs,","venue":null,"work_id":"15dc4c89-01f7-484d-bede-c21ee5fbdf2c","year":2010},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.144613Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:cf284d9dc61e9f7c8dcad297993fda18fe86241f2d281dd24ecbb2edf2bc7f88","observation_id":"4cbae403-759c-4adb-9452-916f40e5af6f","resolution":{"observed_at":"2026-08-10T21:56:24.015926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.989727Z","title":"libPMacc","venue":null,"work_id":"11b9c2b6-68c5-4375-96f9-c50fcde290ce","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.149762Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:13f147461459a1e78e37584d9f10ee5a0b41843cd6474a0734c1d68fbe745fa6","observation_id":"4b8e278c-8f52-4e23-aa55-cc20db28062c","resolution":{"observed_at":"2026-08-10T21:56:23.994152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.974437Z","title":"openpmd-api: C++ & python api for scientific i/o with openpmd,","venue":null,"work_id":"6490e393-bc52-487a-bc42-349b0f770086","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.154016Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:be60aec184089a1e1d73ffb19531ac719fa05f4fbf14662f75840ae65be413ff","observation_id":"7d08f8a0-7ae9-4bae-af77-6ecd16d6fcf1","resolution":{"observed_at":"2026-08-10T21:56:23.979905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.959039Z","title":"openPMD 1.1.0: Base paths for mesh- and particle- only files and updated attributes,","venue":null,"work_id":"1e2898df-ed54-49e5-afd4-e2f46f7c1cda","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.160270Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:ab669d78852c2f29eb4a926c346adcb2cd314a89dead36161b90624e9df64347","observation_id":"37dd7b3c-e099-4851-a6f8-0b92c8729867","resolution":{"observed_at":"2026-08-10T21:56:23.964660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.944290Z","title":"Adios 2: The adaptable input output system. a framework for high- performance data management,","venue":null,"work_id":"ca85b805-3b89-4c39-b28b-41ff73cd7183","year":2020},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.166809Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:73dfef156c2e604b1f61365ae4160328f27d4a1438ea1154da8dad77b5b648b8","observation_id":"684b8701-db08-495b-ac69-494d8add3ba3","resolution":{"observed_at":"2026-08-10T21:56:23.949210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.926789Z","title":"How to test and verify radiation diagnostics simulations within particle-in-cell frameworks,","venue":null,"work_id":"cb7b10e2-c46d-4be9-ad8e-98e0ba75b82b","year":2014},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.171353Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:6fd5637d7de425335328ac08a0102564dd0f189e9f137624ac4902d1667752e8","observation_id":"136e9a0d-bbaf-48e3-8990-ea618428cb8c","resolution":{"observed_at":"2026-08-10T21:56:23.932671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.910042Z","title":null,"venue":null,"work_id":"2b645003-e795-40fa-b586-f17c0035bfe7","year":1998},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.176391Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:f11f7599acc2a2fa344c8c54a2977cc8753e20e986b0c17e03fb3c33b8458f4d","observation_id":"1b8101f2-7759-453e-973d-1cc89bec0a56","resolution":{"observed_at":"2026-08-10T21:56:23.914218Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.895386Z","title":"Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles,","venue":null,"work_id":"206ae06d-e0ba-483d-a488-7e143b69277d","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.180301Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:fab0f65d311557fc6bf539e70de9f5e5dfec00a528fb566d67ec7fc74b188046","observation_id":"fe9cd853-313b-4a3c-9d44-966a8ce05aba","resolution":{"observed_at":"2026-08-10T21:56:23.900165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.880993Z","title":"Computing Angularly-Resolved Far-Field Emission Spectra In Particle-In-Cell Codes Using GPUs,","venue":null,"work_id":"ae973f12-bc1f-48b3-be63-e774dafc0d00","year":2014},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.184298Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e876ca4b8df301ab62bfa2e22b90b0edaf48dac3c8bc0793d31230c6882a4302","observation_id":"8c046b01-b89d-41f2-bf7c-77035d520b83","resolution":{"observed_at":"2026-08-10T21:56:23.885142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.867741Z","title":"Overview on projects around openPMD","venue":null,"work_id":"7371035c-2bc8-4bbe-8126-370a38c75e96","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.189771Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:d235d4f8f506bb317cbba0af7242b5dafd00ee2b8846d61e7bcde4ca2f7319f0","observation_id":"90a13598-eebc-49ce-a7f4-fe2d1b7c7ef4","resolution":{"observed_at":"2026-08-10T21:56:23.872501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.855317Z","title":"Understanding the impact of data staging for coupled scientific workflows,","venue":null,"work_id":"631b8c7f-f13a-42d6-9634-c01d99c59590","year":2022},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.194419Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:a92a406b18ced7ec768a1ff4d07345d41ba7a8914dbfcada5e322fb1e2bb7ac1","observation_id":"0b8f257d-ce21-477a-afbb-4405d4f671ba","resolution":{"observed_at":"2026-08-10T21:56:23.859331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.843991Z","title":"Stream- ing data in hpc workflows using adios,","venue":null,"work_id":"583eadfe-bc1c-44bb-9560-4325e89dfaec","year":2024},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.198941Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:5a3a55de9026c10aa2c9b27f3b17a2483e1ebfed41fa2c0345934f3f05eb1571","observation_id":"60d9df3d-5b89-40d7-b682-ae23ae6a47f2","resolution":{"observed_at":"2026-08-10T21:56:23.847998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.831333Z","title":"Open Fabric Interfaces","venue":null,"work_id":"f19c3592-a6f0-447e-b59b-4be10f3d96c2","year":null},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.202937Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:4d8c4ecbb64df45e251f37b6ba914fedccd2e17b10611fa98e6de400ff95f280","observation_id":"0299eb61-f3f0-4629-8be9-93dc935375f8","resolution":{"observed_at":"2026-08-10T21:56:23.835095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.818564Z","title":"OLCF announces storage specifications for frontier exascale system","venue":null,"work_id":"2869cf9c-2b2b-43fe-b809-705062540caf","year":2021},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.207755Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:88b593f148040131b0a7eb607186c0fd5878ec9e39475ef6681e711e51bdc1f6","observation_id":"f3b2557b-9534-4871-93e5-f4fe2bc24ff3","resolution":{"observed_at":"2026-08-10T21:56:23.823618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.803673Z","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,","venue":null,"work_id":"dba01f72-c849-40f9-90c1-3942304083dd","year":2016},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.211420Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:9dcfd4f13dcbf36e80e18d5151b97a132f065822363657e0f6345e8629d7326c","observation_id":"9f9851d5-3dc3-4d2b-814f-70e4c299529e","resolution":{"observed_at":"2026-08-10T21:56:23.809039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08803","last_updated":"2017-02-27T23:21:10Z","snapshot_observed_at":"2026-08-10T10:35:06.780085Z","submitted_at":"2016-05-27T21:24:32Z","title":"Density estimation using Real NVP","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08803","snapshot_observed_at":"2026-08-10T21:56:23.215394Z","title":"Density estimation using Real NVP,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.215394Z"},"links":{"cited_paper":"/paper/1605.08803","citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:1e2031384023516c212f7f174e7c22d27bd7b8a3ada486c7596b343da4e5ae72","observation_id":"62746961-6382-4bca-a7bc-a1b18a4cb1c6","resolution":{"observed_at":"2026-08-10T21:56:23.215394Z","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":"2026-08-10T21:56:23.787326Z","title":"Structured Output Learning with Conditional Generative Flows,","venue":null,"work_id":"3deb3e58-9a5c-4f5f-aba0-75d4a511ce8b","year":2020},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.219970Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:b2bf1748143441a9813eea04bf8a3d0a28e365b7beca38cb15239eca7af2f327","observation_id":"ec63f134-c116-45fd-ad34-a5e862e47afe","resolution":{"observed_at":"2026-08-10T21:56:23.792734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.04730","last_updated":"2019-02-06T15:45:02Z","snapshot_observed_at":"2026-07-06T06:55:37.123626Z","submitted_at":"2018-08-14T14:58:59Z","title":"Analyzing Inverse Problems with Invertible Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.04730","snapshot_observed_at":"2026-08-10T21:56:23.223639Z","title":"Analyzing Inverse Problems with Invertible Neural Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.223639Z"},"links":{"cited_paper":"/paper/1808.04730","citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:b8659d5a0be306eb7d27f985454eb7f20d43d1297228c33b1e055dc70308d821","observation_id":"f0802e47-7c45-4d60-836b-4bef1215936e","resolution":{"observed_at":"2026-08-10T21:56:23.223639Z","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":"2026-08-10T21:56:23.772513Z","title":"Density estimation by dual ascent of the log-likelihood,","venue":null,"work_id":"a846adca-78e4-4191-88c7-9bf92868a9b4","year":2010},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.228869Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:4b20b51ba1a5f77c86ff44a746e3fd3acc37d8f9ab965dc32d2d817ded5ac29a","observation_id":"98570c27-43a0-4083-bacd-a49216f5f367","resolution":{"observed_at":"2026-08-10T21:56:23.778129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.758802Z","title":"A Family of Nonparametric Density Estimation Algorithms,","venue":null,"work_id":"5d5a7a47-b417-4884-b18a-4a8685d0ee2e","year":2013},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.233849Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e445147686db290cd0ba15996671d3b5975c7c6f67ab24682c9c90d20a826d3d","observation_id":"cfa4090d-1259-4e87-bd8c-f1784ba4ef02","resolution":{"observed_at":"2026-08-10T21:56:23.763220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.624578Z","title":"NICE: Non-linear Independent Components Estimation,","venue":null,"work_id":"595c41a0-0b2b-4413-8a93-04215cd23a68","year":2015},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.238258Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:c0aaa779beb2418128d225666a871fcca4eac72c959aada3366c05027b0658e5","observation_id":"e7bf2726-0f64-45f7-b760-f9e502ef75f7","resolution":{"observed_at":"2026-08-10T21:56:23.749494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.609763Z","title":"Generative Models for 3D Point Clouds,","venue":null,"work_id":"1a903692-469a-431e-8e38-f1ce29c55d6f","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.243063Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e59a413f3255ba380aba4e382eaee6a631cffc2a5ecf7551ab2b4571455e7ca4","observation_id":"92f481d3-0b09-4105-8944-d59e5142b410","resolution":{"observed_at":"2026-08-10T21:56:23.615841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.594554Z","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,","venue":null,"work_id":"59ff4a1b-ae45-49a1-93df-d1bad3b5eb48","year":2017},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.248592Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:4d8055445f100459dc888de2a9769742d2c5d1272e1f6fbc5ea2018590c22dd7","observation_id":"289a5c21-86b8-4712-9414-e4880922cf24","resolution":{"observed_at":"2026-08-10T21:56:23.599501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.579151Z","title":"Glow: Generative Flow with Invertible 1x1 Convolutions,","venue":null,"work_id":"c0eda0ee-ca7a-4f73-b48f-4027f268832b","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.254100Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:3c78516abcad1cfc40d3789ee109753b22b98686728cf7da7fdc03de02c35921","observation_id":"5e463e25-fcfd-44c7-b85c-a343e7758953","resolution":{"observed_at":"2026-08-10T21:56:23.583793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.564323Z","title":"A Point Set Generation Network for 3D Object Reconstruction from a Single Image,","venue":null,"work_id":"332447e7-3973-445a-9995-310613e5ada1","year":2016},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.259678Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:ea238db3e68e4183cf4e43d2c055bd4d5d5992f0d1b4c624e589364bc5c67a5b","observation_id":"2003ebcb-5ce2-482b-9e19-722679f06634","resolution":{"observed_at":"2026-08-10T21:56:23.569779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.549434Z","title":"Auto-Encoding Variational Bayes,","venue":null,"work_id":"7aff9a06-39cc-4c70-bfc3-bbd803253ca3","year":2013},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.264712Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:dd7bb37e7c61108f1d6628909ffab09f191731974be37b40cbdc0f30bc846d5d","observation_id":"34c1aacd-4e1a-4865-897a-6450ea784487","resolution":{"observed_at":"2026-08-10T21:56:23.554581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.535127Z","title":"On tiny episodic memories in continual learning,","venue":null,"work_id":"4a346bc0-01f2-425f-9ccc-b64535f59957","year":2019},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.269759Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:9699234d6b3397cbf409594f362300aedaccec7bd19077c11ed85fb373f8919a","observation_id":"93dd395d-d8a0-4d57-a9a3-f448f1de7284","resolution":{"observed_at":"2026-08-10T21:56:23.539966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.274252Z","title":"Adam: A Method for Stochastic Optimization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.274252Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:74ab46c65956fd871244a67423223c69bc7830e68f23265cbfcde6b388046bda","observation_id":"d0fc6144-cbca-4caf-90cd-d438e9f124c0","resolution":{"observed_at":"2026-08-10T21:56:23.274252Z","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":"2026-08-10T21:56:23.513876Z","title":"One weird trick for parallelizing convolutional neural networks,","venue":null,"work_id":"e2aa918b-7ef4-4158-8fa7-5a6995e7597f","year":2014},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.279372Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:b627e282586340f484e30125db5b827c26f5ab8955727e3e17f75cd199d1d52f","observation_id":"d71384f2-4475-40b6-93a9-1c7d8dc214a4","resolution":{"observed_at":"2026-08-10T21:56:23.519142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.500397Z","title":"Learning Rep- resentations and Generative Models for 3D Point Clouds,","venue":null,"work_id":"4d7b981b-f13a-4400-8a7a-b823f73ee306","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.284618Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:0718b6aedc9cb27ab3c763695a2dc1b432c87d7914ec1b91b425131332f8c813","observation_id":"5e351b80-cb6a-4f71-b3a4-ccd6a9a7b7c6","resolution":{"observed_at":"2026-08-10T21:56:23.504724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.487338Z","title":"The Earth Mover’s Distance as a Metric for Image Retrieval,","venue":null,"work_id":"d8805f93-893e-437c-a110-8cc8e89fd022","year":2000},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.290733Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:0418574e0d04f4f75860d9a888be013a9f839ffb281d2659304776cba1b884d0","observation_id":"53ba520c-98ed-4729-98c1-aa07503f6e86","resolution":{"observed_at":"2026-08-10T21:56:23.492093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.471107Z","title":"Interpolating between Optimal Transport and MMD using Sinkhorn Divergences,","venue":null,"work_id":"4eecf532-544b-4a96-a3e4-6c4677c67dfc","year":2019},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.295143Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:91b8fae150ff6988802fc9525843df09f8916dcdcf9373c752fa2ba3abdd5c78","observation_id":"001c2208-f2ee-4a05-b0a4-ad67134e8afa","resolution":{"observed_at":"2026-08-10T21:56:23.476901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.455558Z","title":"Kernel Operations on the GPU, with Autodiff, without Memory Overflows,","venue":null,"work_id":"47b242bb-8c4c-4fdd-ad11-2df13779ddf4","year":2021},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.300481Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:05b0dedc429e4f16bccda967dc2dc98ce25919c95eeeb9ac23ea6f2f15f52934","observation_id":"875bfaf0-eae7-45d0-bb6e-ed6847ee5b67","resolution":{"observed_at":"2026-08-10T21:56:23.460759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.441758Z","title":"Fast geometric learning with symbolic matrices,","venue":null,"work_id":"ca11b4bc-5753-4cf0-8ddc-527884c5048c","year":2020},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.305444Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e947d5be84a97f6df9c4aabc3f38e4b008d65661a82be9caff91c9572d7a228d","observation_id":"aee4f62b-3086-46f2-a382-6411d183b940","resolution":{"observed_at":"2026-08-10T21:56:23.446607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.425991Z","title":"Optimizing distributed training on frontier for large language models,","venue":null,"work_id":"537e92d5-257b-47e9-8ecb-140fd92a7626","year":2023},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.310380Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:386b444ca85104872630686547a34d1dde34b9abfdca5456b97bad93b0324bfb","observation_id":"2a6dfda6-2e32-45e3-b2cd-5c606e4d92c3","resolution":{"observed_at":"2026-08-10T21:56:23.430997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.409417Z","title":"Learning representations and generative models for 3D point clouds,","venue":null,"work_id":"81148308-34c0-468c-a3e0-56cb2bb1c1f6","year":2018},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.315213Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:d96c061d058313257bde18104ced4b46aacef56d899b528292fa304bff7edafd","observation_id":"13bd5761-f310-49c4-932b-afb313a3869e","resolution":{"observed_at":"2026-08-10T21:56:23.414535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.392468Z","title":"Emerging Properties in Self-Supervised Vision Transform- ers,","venue":null,"work_id":"ebd525f2-7890-41d3-8346-5c9de6778201","year":2021},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.319701Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:e5f05b60a65b7ac465999ecd8be0c48792d75948a3c189a4c401328fba8425b3","observation_id":"3d414363-7a41-48ce-a384-6796c7af1bb4","resolution":{"observed_at":"2026-08-10T21:56:23.400082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T21:56:23.044909Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations","version":3},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-10T21:56:23.044909Z"},"links":{"citing_paper":"/paper/2501.03383"},"observation_digest":"sha256:978e764a444b2649f7430e79e1cc3da3b3af0dbf039ce100e57cc9bc5d782bc7","observation_id":"3443152f-0cc3-43aa-ba47-a32e797dabd4","resolution":{"observed_at":"2026-08-10T21:56:23.044909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.03383","last_updated":"2025-07-03T08:40:46Z","latest_version":3,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-08-10T21:50:29.239899Z","submitted_at":"2025-01-06T20:58:27Z","title":"The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":61},"total_outbound_references":69},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2501.03383."}