{"as_of":"2026-08-08T18:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e061dfeaf87c12e68f4a435bc205baec4e0c9d942ec27b58fecf8b6023dba8ea","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T16:53:17.681575Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.01305/citation-record","integrity":"/paper/2606.01305/integrity","json":"/paper/2606.01305/citation-record.json","paper":"/paper/2606.01305"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Calphad82, 102580 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:c4d20b1a8faa394784c5e31ed5578edac141ba9912f2b3e4ba99ca28256f4037","observation_id":"68d65126-b629-463a-ac46-ae62d1ebbf79","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Jom49, 14–19 (1997)","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:8bd123c6ffbd1ab20833cb071aea1b89fc2f46c5e13f3745b5f6e8041aec8c1a","observation_id":"9af52574-f7e3-4e72-be1f-6ca059623acb","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Calphad26(2), 273–312 (2002)","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:c2a5421f9066dd8dbd77dbc60d5b0ba0b1f80ec527e07b9ab81b74a0556cb264","observation_id":"67a75e99-e6f9-487f-82d6-125a82445d76","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Calphad 26(2), 175–188 (2002)","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:5832708feca6f0995083de78cbae4b97c3e6edb744a70bec2a08a28c3e3d4e9b","observation_id":"b1a0e19d-9e54-4bc7-9734-7c33c0b9d680","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Integrating Materials and Manufacturing Innovation4(1), 1–15 (2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:e923856bd708ef53d84b484c485e9476a0394bbdbea70006c3137937abbaae00","observation_id":"8282d5f5-c618-41fa-aabd-a1c6324ef2ab","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Materials Science and Engineering: R: Reports146, 100644 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:5d5490c2424ec7c3169bcbe6ddc39ee1a3dfcdb511042db462326ca14b2965cb","observation_id":"705cde7d-479f-4e44-9c5f-4ffafe3fa875","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Materials & design202, 109532 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:94a37d91e456225a48c718961c47e7f8169806b765150bb3f6949d9f7d446e29","observation_id":"7c5c1c1a-c63c-40c6-b9e6-42d72cfdb503","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Scripta Materialia249, 116180 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:2e423746373d296854da6f20abcb21adfe01202ce449ce46f7fe023a88f04fe0","observation_id":"07750eeb-11ed-410a-9aa9-4fbd80006c58","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Journal of Alloys and Compounds960, 170543 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:ee7bb8f8906cada00afb0717607363f007df3f3edb82cd413064f683e1f10f15","observation_id":"9d26bcbc-98de-4260-bf26-5fd5e148e4d4","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Acta Materialia198, 178–222 (2020) 21","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:8a21ca85d4259523790ca366f1a08b264f175c684e6a46573562eeb640f31dbb","observation_id":"267136b3-3cb9-4de9-bf2c-647d99142761","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Journal of Materials Research and Technology26, 1341–1374 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:46b6e7e8c3faee665de005c756d210fe45ce2d4e09714e6f02bac1fa4bad12ff","observation_id":"dd7697ea-3d7e-4d3d-96b2-66d1d52d5ba1","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Journal of Materials Research and Technology30, 5381–5393 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:a759b586a90e4138505a5d0ecbcd70c69f74110eaad720c4ad2801fa0f176eea","observation_id":"76dae038-a353-49c4-bc28-f0ebda0f5278","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Materials Today Communications30, 103172 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:7820f1507eb8a569c0eb3b3ba255e6cc69eedcd54dd89fbcec6978b56f0eca4d","observation_id":"e3f0b72a-2105-44c7-a4e5-d49c825e1778","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"npj Computational Materials6(1), 50 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:9cdf416004defa78c7363bb59f429c751373696925abc87630a312fad8a41dfd","observation_id":"07072f06-53ce-4eab-bc02-0f22ec69cae6","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"npj Computational Materials9(1), 68 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:872419bd90345ff8c6774c114908ff3676bb00aa7d05f190b10ce9911b1325fe","observation_id":"a8af110b-84fe-4c7a-91e7-bcf7f876a389","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"npj Computational Materials10(1), 172 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:5df505742f88c8aa7c6a57eb71ec6c5aad7a265967b0e0ea91720ab997bb0160","observation_id":"ee636a7d-9751-4a06-86a4-c0ba659ab1d3","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"npj Computational Materials10(1), 274 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:8ccf47e7a1b5adb4604ce9d425faa4710f5c60838c3f28b874c12ca8c8ba3ac2","observation_id":"ca2e2407-7171-43f8-89c6-9b36f0ab571c","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Scientific Reports9(1), 15501 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:9987f789919509d552b811e68c63005baf4d623e807535fb4688260470933fd7","observation_id":"4d149b8b-bad4-4d9a-a7a7-7761edc00ddf","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"MRS Bulletin47(2), 158–167 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:ae978408c9c4294a1b06d9c2ec153d50c2a695d2f31ae336f9b38c1222fdfcb1","observation_id":"4c041beb-a4c4-48e1-9b86-9fc11c14d06b","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Calphad15(4), 317–425 (1991)","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:ece817d785f8825a153ecc19ee87718ea77c5e77b665de50a08aba00424505ad","observation_id":"7ecb08bc-e7e9-4e15-9d9b-4780faba1305","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Computational Materials Science258, 113970 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:4641d1bcfdff8641321d2f16bf1515fa24de084bae8161a91c1ab15a35349667","observation_id":"465cefc6-ea7f-4ed0-8f43-1974549c34e1","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04283","last_updated":"2023-07-10T00:06:31Z","snapshot_observed_at":"2026-08-05T10:45:14.167193Z","submitted_at":"2023-07-10T00:06:31Z","title":"Deep learning for CALPHAD modeling: Universal parameter learning solely based on chemical formula","version":1},"cited_work":{"arxiv_id":"2307.04283","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.04283","snapshot_observed_at":"2026-07-01T21:26:16.536866Z","title":"arXiv preprint arXiv:2307.04283 (2023)","venue":null,"work_id":"bf989565-6ba6-4866-9766-fc9648547d51","year":2023},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"cited_paper":"/paper/2307.04283","citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:dda0eeab4d7555db84c9587fe9ec2042acb2d1f10393a40804e3546baa9ecd24","observation_id":"bbb22e26-a04d-4475-8883-92dcd4fdd8f1","resolution":{"observed_at":"2026-07-01T21:26:16.538445Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06505","last_updated":"2020-03-13T23:10:39Z","snapshot_observed_at":"2026-07-06T09:04:37.917150Z","submitted_at":"2020-03-13T23:10:39Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","version":1},"cited_work":{"arxiv_id":"2003.06505","doi":"10.48550/arxiv.2003.06505","metadata_source":"pith","pith_arxiv_id":"2003.06505","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","venue":"stat.ML","work_id":"32ca4e6c-bd72-4586-8594-40eb6bcb6582","year":2020},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"cited_paper":"/paper/2003.06505","citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:ddc566d59cb106d2950487ced9f65d5dbdc0c7358b9cab0c7cc0e878cf6fc1b2","observation_id":"c344ce2f-2007-4ba1-afda-78e419d6c10a","resolution":{"observed_at":"2026-07-01T21:26:16.541309Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"npj Computational Materials2(1), 1–7 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:3a64f1eabfc4804834cd678f4ba836d6796a29d138cf5fb52fcba61c21c9b431","observation_id":"7f0696d2-ce33-4cd4-98f5-94f87b2d1b14","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Computational Materials Science152, 60–69 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:33e851856eb79b28a06712b913b13c0732b4ff810b16a11c664f0f25cfca0143","observation_id":"910575b7-1949-484b-9fbd-f913b01c56b1","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Journal of Physics: Condensed Matter29(27), 273002 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:772ff17be6e19bff092f34d15f1cf838e9aec42086f2097d4b8c78e5e13bb041","observation_id":"14b13879-d3e6-41c8-9a08-0947b49c7b58","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:ff784a163b8f720635da220d0168c4eaa80e276a421e8feda1e9aa0cfd2a9e15","observation_id":"7b73b9ce-0a12-4d59-89c5-760e82edda8f","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04967","last_updated":"2024-05-10T16:49:52Z","snapshot_observed_at":"2026-08-06T05:52:39.645210Z","submitted_at":"2024-05-08T11:13:30Z","title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures","version":2},"cited_work":{"arxiv_id":"2405.04967","doi":"10.48550/arxiv.2405.04967","metadata_source":"pith","pith_arxiv_id":"2405.04967","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures","venue":"cond-mat.mtrl-sci","work_id":"ee6227fe-2fc7-49bc-990c-aae7db05c4a4","year":2024},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"cited_paper":"/paper/2405.04967","citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:4986b7ee26c07e19842ab29b512f9a2c5a27579a2abd2ed4a97e88f6049c4727","observation_id":"cddad8a9-938d-49dc-bc8d-5b383e43a84c","resolution":{"observed_at":"2026-07-01T21:26:16.544055Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-05-19T23:52:01.523568+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T23:52:01.523568+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Journal of British Surgery105(10), 1348–1348 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:2cc0c9eb8641fecb983384725bbdd9c70e4305dbff8081622c11a82795999c92","observation_id":"34e30cdf-6ce3-4c2c-9ebb-9fd5ad539f8e","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"the Journal of machine Learning research12, 2825–2830 (2011)","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:3d39cb1a755ee19c4756ae3cb1a43a00b898d61a02c5c5500516a5aca9d7e137","observation_id":"ec883373-81ad-40b6-8885-117fe849bd85","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Thermochimica acta129(1), 71–75 (1988)","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:99a6f13e43cab4527a8a24e35ab32c2006b84312c30535ec3aaca36ebdadf873","observation_id":"aa371750-e48e-4fbc-bb97-da12f113389d","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T16:53:17.681575Z","title":"Computational Materials Science68, 314–319 (2013) 23","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T16:53:17.681575Z"},"links":{"citing_paper":"/paper/2606.01305"},"observation_digest":"sha256:dfea07c32dfe9af4458ce2eb5d73a3ae1aa5273d10b919b874172b9929c90861","observation_id":"950a308a-2409-40e3-b555-ce6c63b7a9da","resolution":{"observed_at":"2026-06-28T16:53:17.681575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.01305","last_updated":"2026-05-31T15:49:12Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-07T14:58:34.798925Z","submitted_at":"2026-05-31T15:49:12Z","title":"How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":29,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":32},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2606.01305."}