{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:262WEERSZ7PI4LL2LTDO6V6JAG","short_pith_number":"pith:262WEERS","schema_version":"1.0","canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","source":{"kind":"arxiv","id":"1705.04600","version":2},"attestation_state":"computed","paper":{"title":"Concussion classification via deep learning using whole-brain white matter fiber strains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Shaoju Wu, Songbai Ji, Wei Zhao, Yunliang Cai, Zhigang Li","submitted_at":"2017-05-12T14:40:59Z","abstract_excerpt":"Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fi"},"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":"1705.04600","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-05-12T14:40:59Z","cross_cats_sorted":[],"title_canon_sha256":"44b9089e65e3bc45fbb6b63a253eb2b103ca4dc9ef39d74fcd64176ad8852584","abstract_canon_sha256":"215834d88cf007c8b501ef465e9c6f2595fb04f89c328640624c87afe265e753"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:00.065969Z","signature_b64":"Mbe2LLb0LoGPitdzNpcgltYf9PpjDMt6OgUzTRSlDQnqHbObRUPRkxM6d/o4jg4PYvKJRG2do4plvcJBrArLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","last_reissued_at":"2026-05-18T00:15:00.065260Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:00.065260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Concussion classification via deep learning using whole-brain white matter fiber strains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Shaoju Wu, Songbai Ji, Wei Zhao, Yunliang Cai, Zhigang Li","submitted_at":"2017-05-12T14:40:59Z","abstract_excerpt":"Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04600","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":""},"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":"1705.04600","created_at":"2026-05-18T00:15:00.065380+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.04600v2","created_at":"2026-05-18T00:15:00.065380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04600","created_at":"2026-05-18T00:15:00.065380+00:00"},{"alias_kind":"pith_short_12","alias_value":"262WEERSZ7PI","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"262WEERSZ7PI4LL2","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"262WEERS","created_at":"2026-05-18T12:30:55.937587+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/262WEERSZ7PI4LL2LTDO6V6JAG","json":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG.json","graph_json":"https://pith.science/api/pith-number/262WEERSZ7PI4LL2LTDO6V6JAG/graph.json","events_json":"https://pith.science/api/pith-number/262WEERSZ7PI4LL2LTDO6V6JAG/events.json","paper":"https://pith.science/paper/262WEERS"},"agent_actions":{"view_html":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG","download_json":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG.json","view_paper":"https://pith.science/paper/262WEERS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.04600&json=true","fetch_graph":"https://pith.science/api/pith-number/262WEERSZ7PI4LL2LTDO6V6JAG/graph.json","fetch_events":"https://pith.science/api/pith-number/262WEERSZ7PI4LL2LTDO6V6JAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/action/storage_attestation","attest_author":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/action/author_attestation","sign_citation":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/action/citation_signature","submit_replication":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/action/replication_record"}},"created_at":"2026-05-18T00:15:00.065380+00:00","updated_at":"2026-05-18T00:15:00.065380+00:00"}