{"id":"W4417305353","doi":"10.1007/s10237-025-02034-6","title":"Large-scale modeling of axonal dynamic responses via deep learning","year":2025,"lang":"en","type":"article","venue":"Biomechanics and Modeling in Mechanobiology","topic":"Automotive and Human Injury Biomechanics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Canadian Institutes of Health Research; National Institutes of Health; National Science Foundation","keywords":"Axolemma; Convolutional neural network; Diffuse axonal injury; Deep learning; Pattern recognition (psychology); White matter; Similarity (geometry)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009293525,0.0002656427,0.0006515908,0.0009883427,0.0001465919,0.00001065398,0.0001438785,0.0004240941,0.00003340214],"category_scores_gemma":[0.00009447779,0.0002543873,0.0001257051,0.0004248011,0.00003875341,0.00004450165,0.0002339736,0.0004061558,0.000003912367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009424322,"about_ca_system_score_gemma":0.000115138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004051468,"about_ca_topic_score_gemma":0.00007973271,"domain_scores_codex":[0.9980257,0.0001295758,0.000645355,0.0005716088,0.0001342458,0.0004934853],"domain_scores_gemma":[0.9992852,0.00006694318,0.0001166383,0.0002728487,0.0001744605,0.0000839587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002264997,0.0006228575,0.0001819888,0.000595268,0.0002592475,0.00003902606,0.001570451,0.008280145,0.9203119,0.04253509,0.00000539062,0.02333364],"study_design_scores_gemma":[0.001359156,0.0005306591,0.000004212926,0.0002098576,0.00008080068,0.00002847816,0.0008803881,0.9616144,0.004143191,0.03083437,0.0001178087,0.0001966934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4439473,0.001013128,0.5543589,0.000125782,0.0001909718,0.0002168362,0.00001332101,0.00004641495,0.00008734276],"genre_scores_gemma":[0.9931951,0.0009414873,0.00517297,0.000264551,0.00002481123,0.00002744078,0.00007349544,0.00002881641,0.000271353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9533343,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375058508589077,"score_gpt":0.2973031611738544,"score_spread":0.2735525760879636,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}