{"id":"W2755998014","doi":"10.1016/j.jneumeth.2017.09.007","title":"High-speed video analysis improves the accuracy of spinal cord compression measurement in a mouse contusion model","year":2017,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Automotive and Human Injury Biomechanics","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Hôpital du Sacré-Cœur de Montréal","funders":"Institut français des sciences et technologies des transports, de l’aménagement et des réseaux; Agence Nationale de la Recherche","keywords":"Spinal cord compression; Spinal trauma; Spinal cord; Spinal cord injury; Compression (physics); Computer science; Medicine; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005526327,0.0005139631,0.0003354232,0.001040183,0.0002572903,0.000551179,0.0004624557,0.0008029546,0.001850916],"category_scores_gemma":[0.001081786,0.0002766539,0.0002633513,0.0003531981,0.0002541142,0.0006965854,0.0003754484,0.000587289,0.0003900658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003144943,"about_ca_system_score_gemma":0.0005169503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003325926,"about_ca_topic_score_gemma":0.004246396,"domain_scores_codex":[0.9996498,0.00003498881,0.00001919873,0.00008769684,0.000163655,0.00004478561],"domain_scores_gemma":[0.9991406,0.0001870987,0.0001680821,0.00006847438,0.0003735632,0.00006221068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005754349,0.00009446649,0.002451673,0.0001210471,0.00002280452,0.00007468633,0.00003941474,0.0008114144,0.9616044,0.0001129928,0.0004605363,0.03363103],"study_design_scores_gemma":[0.00005202367,0.0008975698,0.04589712,0.00004850934,0.0001539222,0.0005087026,0.00009977449,0.06734429,0.8830726,0.0002529303,0.001611462,0.00006107945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8629202,0.002774166,0.1296403,0.0004126482,0.0002443277,0.000122753,0.0005541647,0.001312534,0.002018909],"genre_scores_gemma":[0.959555,0.001357247,0.0358534,0.0001435666,0.00005838528,0.00009718772,0.0003601575,0.0001239089,0.002451163],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003325926,"threshold_uncertainty_score":0.006613135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1953036593892068,"score_gpt":0.459923454933199,"score_spread":0.2646197955439922,"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."}}