{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CR7CD4SHGVYHI2T5V5P3I6Q3GN","short_pith_number":"pith:CR7CD4SH","schema_version":"1.0","canonical_sha256":"147e21f2473570746a7daf5fb47a1b33685f70bd674ded02f7bcbee4830c679d","source":{"kind":"arxiv","id":"2502.07107","version":2},"attestation_state":"computed","paper":{"title":"A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"stat.AP","authors_text":"Daniel W. Apley, Kungang Zhang, L. Catherine Brinson, Wei Chen, Wing K. Liu","submitted_at":"2025-02-10T23:05:35Z","abstract_excerpt":"Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to classify micrographs according to microstructure phase/class and, for multiphase microstructures, segments them into different homogeneous regions. With the advance of manufacturing and imaging techniques, the ultra-high resolution of imaging that reveals the complexity of microstructures and the rapidly increasing quantity of images (i.e., micrographs) enables a"},"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":"2502.07107","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-02-10T23:05:35Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"14fc3a033dcb73ea784ea90db5fc50c39c174582bd2946e35e9ea7dc394abcb6","abstract_canon_sha256":"003c7cd2a2cc65d4c7ed8201616e91ff72cb5fe1cac807cd69f5dca888b9e74b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:31.148058Z","signature_b64":"502VhBJanfm+50pjqXmsj0e72Rbcy0K0O2YM3oJKEcNcyQW7fj3qsIyYqfVTJT/zgeo+jQ5RTZTSx2WhskBcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"147e21f2473570746a7daf5fb47a1b33685f70bd674ded02f7bcbee4830c679d","last_reissued_at":"2026-07-05T12:04:31.147452Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:31.147452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"stat.AP","authors_text":"Daniel W. Apley, Kungang Zhang, L. Catherine Brinson, Wei Chen, Wing K. Liu","submitted_at":"2025-02-10T23:05:35Z","abstract_excerpt":"Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to classify micrographs according to microstructure phase/class and, for multiphase microstructures, segments them into different homogeneous regions. With the advance of manufacturing and imaging techniques, the ultra-high resolution of imaging that reveals the complexity of microstructures and the rapidly increasing quantity of images (i.e., micrographs) enables a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.07107","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/2502.07107/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":"2502.07107","created_at":"2026-07-05T12:04:31.147519+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.07107v2","created_at":"2026-07-05T12:04:31.147519+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.07107","created_at":"2026-07-05T12:04:31.147519+00:00"},{"alias_kind":"pith_short_12","alias_value":"CR7CD4SHGVYH","created_at":"2026-07-05T12:04:31.147519+00:00"},{"alias_kind":"pith_short_16","alias_value":"CR7CD4SHGVYHI2T5","created_at":"2026-07-05T12:04:31.147519+00:00"},{"alias_kind":"pith_short_8","alias_value":"CR7CD4SH","created_at":"2026-07-05T12:04:31.147519+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/CR7CD4SHGVYHI2T5V5P3I6Q3GN","json":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN.json","graph_json":"https://pith.science/api/pith-number/CR7CD4SHGVYHI2T5V5P3I6Q3GN/graph.json","events_json":"https://pith.science/api/pith-number/CR7CD4SHGVYHI2T5V5P3I6Q3GN/events.json","paper":"https://pith.science/paper/CR7CD4SH"},"agent_actions":{"view_html":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN","download_json":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN.json","view_paper":"https://pith.science/paper/CR7CD4SH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.07107&json=true","fetch_graph":"https://pith.science/api/pith-number/CR7CD4SHGVYHI2T5V5P3I6Q3GN/graph.json","fetch_events":"https://pith.science/api/pith-number/CR7CD4SHGVYHI2T5V5P3I6Q3GN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN/action/storage_attestation","attest_author":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN/action/author_attestation","sign_citation":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN/action/citation_signature","submit_replication":"https://pith.science/pith/CR7CD4SHGVYHI2T5V5P3I6Q3GN/action/replication_record"}},"created_at":"2026-07-05T12:04:31.147519+00:00","updated_at":"2026-07-05T12:04:31.147519+00:00"}