{"id":"W15894917","doi":"10.1007/1-4020-3796-1_45","title":"Numerical Human Model to Predict Side Impact Thoracic Trauma","year":2005,"lang":"en","type":"book-chapter","venue":"Solid mechanics and its applications","topic":"Automotive and Human Injury Biomechanics","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"General Motors (Canada); University of Waterloo","funders":"","keywords":"Rib cage; Pelvis; Pendulum; Human-body model; Side impact; Hybrid III; Thoracic spine; Simulation; Thoracic trauma; Crash; Structural engineering; Computer science; Anatomy; Engineering; Surgery; Medicine; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001682826,0.0005531384,0.0007630532,0.0003416687,0.0003549693,0.00005141306,0.0002306143,0.0005236226,0.0004641492],"category_scores_gemma":[0.00001046059,0.0005058501,0.0003113237,0.00009315738,0.00002047695,0.00006833774,0.0001340028,0.0006048708,0.0003171715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205374,"about_ca_system_score_gemma":0.0001787698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003630927,"about_ca_topic_score_gemma":0.0000061619,"domain_scores_codex":[0.9977996,0.000008521599,0.0005677365,0.0007941999,0.0003991381,0.00043079],"domain_scores_gemma":[0.9981313,0.00002498873,0.0002341684,0.0007000843,0.0002654999,0.0006439375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006370095,0.0001742029,2.639933e-7,0.0001358873,0.0002631646,0.000006874721,0.0001817386,0.0001055354,0.004212641,0.9632182,0.003656075,0.02798173],"study_design_scores_gemma":[0.001277565,0.002834535,0.00002396215,0.000686524,0.001971657,0.0002155499,0.00004491207,0.1802823,0.003867012,0.4369385,0.3698107,0.002046858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001199795,0.003885551,0.517395,0.002232209,0.0001688911,0.0100197,0.003116851,0.000912266,0.4610697],"genre_scores_gemma":[0.5562186,0.001016829,0.003985255,0.002129241,0.001777375,0.0008094079,0.00121587,0.0004604412,0.432387],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5550188,"threshold_uncertainty_score":0.9997393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04406155404408182,"score_gpt":0.3532555526542839,"score_spread":0.3091939986102021,"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."}}