{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7VEOAAWQ7LBCHT7EUNRKKLVGTT","short_pith_number":"pith:7VEOAAWQ","schema_version":"1.0","canonical_sha256":"fd48e002d0fac223cfe4a362a52ea69cd8536a34b5bed9cce0b0fc110fefc29e","source":{"kind":"arxiv","id":"2210.15600","version":2},"attestation_state":"computed","paper":{"title":"Automatic extraction of materials and properties from superconductors scientific literature","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.supr-con","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kensei Terashima, Luca Foppiano, Masashi Ishii, Pedro Baptista de Castro, Pedro Ortiz Suarez, Yoshihiko Takano","submitted_at":"2022-10-26T01:03:28Z","abstract_excerpt":"The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records fro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2210.15600","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-26T01:03:28Z","cross_cats_sorted":["cond-mat.supr-con","cs.LG"],"title_canon_sha256":"61f7a9ad0d7d5a3d1e78e36262b5eb3ceb3e59cf4ef9147fedfc0e7743cbaabf","abstract_canon_sha256":"dfe97e3c52275a03a14cf1dd1c33d34f45a83c4c42f5b1a67627edc457bacec6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:49.230706Z","signature_b64":"/jN+AsJNpruwMOjI75AZ4OVcO0+5S4wuZNPJJHYgUYbGZXHSmlUSgtMCXL5I/bmF1onxZy+aR/XHl3RwkcTFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd48e002d0fac223cfe4a362a52ea69cd8536a34b5bed9cce0b0fc110fefc29e","last_reissued_at":"2026-07-05T07:15:49.230262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:49.230262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic extraction of materials and properties from superconductors scientific literature","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.supr-con","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kensei Terashima, Luca Foppiano, Masashi Ishii, Pedro Baptista de Castro, Pedro Ortiz Suarez, Yoshihiko Takano","submitted_at":"2022-10-26T01:03:28Z","abstract_excerpt":"The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15600","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.15600/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2210.15600","created_at":"2026-07-05T07:15:49.230323+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.15600v2","created_at":"2026-07-05T07:15:49.230323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15600","created_at":"2026-07-05T07:15:49.230323+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VEOAAWQ7LBC","created_at":"2026-07-05T07:15:49.230323+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VEOAAWQ7LBCHT7E","created_at":"2026-07-05T07:15:49.230323+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VEOAAWQ","created_at":"2026-07-05T07:15:49.230323+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT","json":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT.json","graph_json":"https://pith.science/api/pith-number/7VEOAAWQ7LBCHT7EUNRKKLVGTT/graph.json","events_json":"https://pith.science/api/pith-number/7VEOAAWQ7LBCHT7EUNRKKLVGTT/events.json","paper":"https://pith.science/paper/7VEOAAWQ"},"agent_actions":{"view_html":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT","download_json":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT.json","view_paper":"https://pith.science/paper/7VEOAAWQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.15600&json=true","fetch_graph":"https://pith.science/api/pith-number/7VEOAAWQ7LBCHT7EUNRKKLVGTT/graph.json","fetch_events":"https://pith.science/api/pith-number/7VEOAAWQ7LBCHT7EUNRKKLVGTT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT/action/storage_attestation","attest_author":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT/action/author_attestation","sign_citation":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT/action/citation_signature","submit_replication":"https://pith.science/pith/7VEOAAWQ7LBCHT7EUNRKKLVGTT/action/replication_record"}},"created_at":"2026-07-05T07:15:49.230323+00:00","updated_at":"2026-07-05T07:15:49.230323+00:00"}