{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WA7GUCC2SLHWUWORZ7PVUXMB4B","short_pith_number":"pith:WA7GUCC2","schema_version":"1.0","canonical_sha256":"b03e6a085a92cf6a59d1cfdf5a5d81e0582c844434bc5dd3c6e7f1c66ddb66a9","source":{"kind":"arxiv","id":"2409.03729","version":3},"attestation_state":"computed","paper":{"title":"SR-CLD: Spatially Resolved Chord Length Distributions for Statistical Description and Visualization of Non-uniform Microstructures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Marat I. Latypov, Sheila E. Whitman","submitted_at":"2024-09-05T17:35:10Z","abstract_excerpt":"This study introduces the calculation of spatially-resolved chord length distribution (SR-CLD) as an efficient approach for quantifying and visualizing non-uniform microstructures in heterogeneous materials. SR-CLD enables detailed analysis of spatial variation of microstructures in different directions that can be overlooked with traditional descriptions. We present the calculation of SR-CLD using efficient scan-line algorithm that counts pixels in constituents along pixel rows or columns of microstructure images for detailed, high-resolution SR-CLD maps. We demonstrate the application of SR-"},"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":"2409.03729","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-09-05T17:35:10Z","cross_cats_sorted":[],"title_canon_sha256":"f666cf24432deea8bc4bc0a375b1d32c25328db06d2822a0482eaff8b67d290e","abstract_canon_sha256":"d2477d7f651f20be65b3ebcc94ac80a70d49ec705e24abb113836e1dccc0bcdf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:07:54.854093Z","signature_b64":"ueGYF8rFPqz4IPyb/Gr+Z6hbznn+6xAJnuFl+ia7FUKeah5o6dLu24D1mncAx4FbqgS9t6jqRQpXLYWTWtYGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b03e6a085a92cf6a59d1cfdf5a5d81e0582c844434bc5dd3c6e7f1c66ddb66a9","last_reissued_at":"2026-07-05T12:07:54.853554Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:07:54.853554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SR-CLD: Spatially Resolved Chord Length Distributions for Statistical Description and Visualization of Non-uniform Microstructures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Marat I. Latypov, Sheila E. Whitman","submitted_at":"2024-09-05T17:35:10Z","abstract_excerpt":"This study introduces the calculation of spatially-resolved chord length distribution (SR-CLD) as an efficient approach for quantifying and visualizing non-uniform microstructures in heterogeneous materials. SR-CLD enables detailed analysis of spatial variation of microstructures in different directions that can be overlooked with traditional descriptions. We present the calculation of SR-CLD using efficient scan-line algorithm that counts pixels in constituents along pixel rows or columns of microstructure images for detailed, high-resolution SR-CLD maps. We demonstrate the application of SR-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03729","kind":"arxiv","version":3},"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/2409.03729/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":"2409.03729","created_at":"2026-07-05T12:07:54.853615+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03729v3","created_at":"2026-07-05T12:07:54.853615+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03729","created_at":"2026-07-05T12:07:54.853615+00:00"},{"alias_kind":"pith_short_12","alias_value":"WA7GUCC2SLHW","created_at":"2026-07-05T12:07:54.853615+00:00"},{"alias_kind":"pith_short_16","alias_value":"WA7GUCC2SLHWUWOR","created_at":"2026-07-05T12:07:54.853615+00:00"},{"alias_kind":"pith_short_8","alias_value":"WA7GUCC2","created_at":"2026-07-05T12:07:54.853615+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.18637","citing_title":"Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B","json":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B.json","graph_json":"https://pith.science/api/pith-number/WA7GUCC2SLHWUWORZ7PVUXMB4B/graph.json","events_json":"https://pith.science/api/pith-number/WA7GUCC2SLHWUWORZ7PVUXMB4B/events.json","paper":"https://pith.science/paper/WA7GUCC2"},"agent_actions":{"view_html":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B","download_json":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B.json","view_paper":"https://pith.science/paper/WA7GUCC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03729&json=true","fetch_graph":"https://pith.science/api/pith-number/WA7GUCC2SLHWUWORZ7PVUXMB4B/graph.json","fetch_events":"https://pith.science/api/pith-number/WA7GUCC2SLHWUWORZ7PVUXMB4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B/action/storage_attestation","attest_author":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B/action/author_attestation","sign_citation":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B/action/citation_signature","submit_replication":"https://pith.science/pith/WA7GUCC2SLHWUWORZ7PVUXMB4B/action/replication_record"}},"created_at":"2026-07-05T12:07:54.853615+00:00","updated_at":"2026-07-05T12:07:54.853615+00:00"}