{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:262WEERSZ7PI4LL2LTDO6V6JAG","short_pith_number":"pith:262WEERS","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"},"canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","source":{"kind":"arxiv","id":"1705.04600","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04600","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04600v2","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04600","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"pith_short_12","alias_value":"262WEERSZ7PI","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"262WEERSZ7PI4LL2","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"262WEERS","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:262WEERSZ7PI4LL2LTDO6V6JAG","target":"record","payload":{"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"},"canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","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"},"source_kind":"arxiv","source_id":"1705.04600","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zkPVbOi+cVQd7UoM7eRG56bE0pV3/UBA6Z4YvaFy00sg03CXdxa216jCIg0Zupt4fYnu2fjQNMt3kMOM5buACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T22:29:19.172506Z"},"content_sha256":"76113b3fc1e615808700f0673392513bf15817c5d9877c920b4dc3f8108ef374","schema_version":"1.0","event_id":"sha256:76113b3fc1e615808700f0673392513bf15817c5d9877c920b4dc3f8108ef374"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:262WEERSZ7PI4LL2LTDO6V6JAG","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wKm+M9cEwmA7zCOYgWF2408F0wDH00adnCIS4mYTj7QmxiHW7HQmgKGG/kBENl+sDWZ3ulzPxwzuI/Xv+/HXDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T22:29:19.172875Z"},"content_sha256":"51772e6f09cb45b62c261dd30d2b5c7d84f615dbb7fae051d8f00399f841aa10","schema_version":"1.0","event_id":"sha256:51772e6f09cb45b62c261dd30d2b5c7d84f615dbb7fae051d8f00399f841aa10"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/bundle.json","state_url":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/262WEERSZ7PI4LL2LTDO6V6JAG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T22:29:19Z","links":{"resolver":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG","bundle":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/bundle.json","state":"https://pith.science/pith/262WEERSZ7PI4LL2LTDO6V6JAG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/262WEERSZ7PI4LL2LTDO6V6JAG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:262WEERSZ7PI4LL2LTDO6V6JAG","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":"215834d88cf007c8b501ef465e9c6f2595fb04f89c328640624c87afe265e753","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-05-12T14:40:59Z","title_canon_sha256":"44b9089e65e3bc45fbb6b63a253eb2b103ca4dc9ef39d74fcd64176ad8852584"},"schema_version":"1.0","source":{"id":"1705.04600","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.04600","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"arxiv_version","alias_value":"1705.04600v2","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.04600","created_at":"2026-05-18T00:15:00Z"},{"alias_kind":"pith_short_12","alias_value":"262WEERSZ7PI","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"262WEERSZ7PI4LL2","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"262WEERS","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:51772e6f09cb45b62c261dd30d2b5c7d84f615dbb7fae051d8f00399f841aa10","target":"graph","created_at":"2026-05-18T00:15:00Z","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"},"paper":{"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","authors_text":"Shaoju Wu, Songbai Ji, Wei Zhao, Yunliang Cai, Zhigang Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-05-12T14:40:59Z","title":"Concussion classification via deep learning using whole-brain white matter fiber strains"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.04600","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":"sha256:76113b3fc1e615808700f0673392513bf15817c5d9877c920b4dc3f8108ef374","target":"record","created_at":"2026-05-18T00:15:00Z","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":"215834d88cf007c8b501ef465e9c6f2595fb04f89c328640624c87afe265e753","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-05-12T14:40:59Z","title_canon_sha256":"44b9089e65e3bc45fbb6b63a253eb2b103ca4dc9ef39d74fcd64176ad8852584"},"schema_version":"1.0","source":{"id":"1705.04600","kind":"arxiv","version":2}},"canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7b5621232cfde8e2d7a5cc6ef57c901a438a30bc79efbc21d147821d64afb40","first_computed_at":"2026-05-18T00:15:00.065260Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:00.065260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mbe2LLb0LoGPitdzNpcgltYf9PpjDMt6OgUzTRSlDQnqHbObRUPRkxM6d/o4jg4PYvKJRG2do4plvcJBrArLDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:00.065969Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.04600","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76113b3fc1e615808700f0673392513bf15817c5d9877c920b4dc3f8108ef374","sha256:51772e6f09cb45b62c261dd30d2b5c7d84f615dbb7fae051d8f00399f841aa10"],"state_sha256":"36415096dc72cb6ee7b2e4a4d44f9bf5c86502f3ee68b11fc80e527698b9f2b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mw5qLc7FWL22bR+NTkh+5dsaV/RIKQvpf5aDjecQeTlmkxzJ1C9gT8mCZLgODnG98Tsqb3ADvRnHhcCdtyXgDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T22:29:19.175500Z","bundle_sha256":"848e4c22898d12814fe21b3b43546b1ff3aac5af541c0d7ff5f4944de84b624c"}}