{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:JNDPAMJ6R3FHPA3WRVKKXPQ62Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c0ee7ba8ea6774076529cf273aedba037b1a6709f6ca76ffc757ed320b916db0","cross_cats_sorted":["cond-mat.soft"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-09-01T13:04:43Z","title_canon_sha256":"0d17d18a1ae483b2b2cd44355cf098aa8713e7520432184e5e99d6fb99fdd825"},"schema_version":"1.0","source":{"id":"2009.03194","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.03194","created_at":"2026-07-05T02:42:26Z"},{"alias_kind":"arxiv_version","alias_value":"2009.03194v2","created_at":"2026-07-05T02:42:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.03194","created_at":"2026-07-05T02:42:26Z"},{"alias_kind":"pith_short_12","alias_value":"JNDPAMJ6R3FH","created_at":"2026-07-05T02:42:26Z"},{"alias_kind":"pith_short_16","alias_value":"JNDPAMJ6R3FHPA3W","created_at":"2026-07-05T02:42:26Z"},{"alias_kind":"pith_short_8","alias_value":"JNDPAMJ6","created_at":"2026-07-05T02:42:26Z"}],"graph_snapshots":[{"event_id":"sha256:0c62364f9e5fd704d3fae14462f65c866f4152785660fb7ffba8467659d07d13","target":"graph","created_at":"2026-07-05T02:42:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2009.03194/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller datasets for each property investigated. Using the decision tree induction, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algori","authors_text":"Andr\\'e C. P. L. F. de Carvalho, Daniel R. Cassar, Edesio Alcoba\\c{c}a, Edgar D. Zanotto, Saulo Martiello Mastelini, Tiago Botari","cross_cats":["cond-mat.soft"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-09-01T13:04:43Z","title":"Predicting and interpreting oxide glass properties by machine learning using large datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.03194","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fc582963b298dbf787e23eb5596479f131d601a76c847055ba8b7ea3e767e530","target":"record","created_at":"2026-07-05T02:42:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c0ee7ba8ea6774076529cf273aedba037b1a6709f6ca76ffc757ed320b916db0","cross_cats_sorted":["cond-mat.soft"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-09-01T13:04:43Z","title_canon_sha256":"0d17d18a1ae483b2b2cd44355cf098aa8713e7520432184e5e99d6fb99fdd825"},"schema_version":"1.0","source":{"id":"2009.03194","kind":"arxiv","version":2}},"canonical_sha256":"4b46f0313e8eca7783768d54abbe1ed4012eb5605552f986a8bdced67483fa35","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b46f0313e8eca7783768d54abbe1ed4012eb5605552f986a8bdced67483fa35","first_computed_at":"2026-07-05T02:42:26.797812Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:42:26.797812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"krtKTPGnM/lisb45l+muuvJAyFwIfGTP7gZbq6A6ACuhKvc46oDEJg2kbsJX4NnrufJJXcMm+Iyok4GevOzXCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:42:26.798241Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.03194","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc582963b298dbf787e23eb5596479f131d601a76c847055ba8b7ea3e767e530","sha256:0c62364f9e5fd704d3fae14462f65c866f4152785660fb7ffba8467659d07d13"],"state_sha256":"5bf2e258217d0c008dd5fa0ec30f7cf94c387f8b9861d3a061e1204eceb75b90"}