{"id":"W2140899707","doi":"10.1016/j.cmpb.2009.03.002","title":"Determining physiological cross-sectional area of extensor carpi radialis longus and brevis as a whole and by regions using 3D computer muscle models created from digitized fiber bundle data","year":2009,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Toronto","funders":"Autodesk","keywords":"Computer science; Bundle; Python (programming language); Programming language; Materials science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002868771,0.0002065077,0.0004583676,0.0001341283,0.0000881065,0.00008405806,0.0001163132,0.0001056563,0.000005675311],"category_scores_gemma":[0.0000142201,0.0001693845,0.00003066015,0.0002654919,0.0002611952,0.0001925156,0.0001178027,0.0001422407,4.721935e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001308177,"about_ca_system_score_gemma":0.000006215402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001217634,"about_ca_topic_score_gemma":0.000001699004,"domain_scores_codex":[0.998799,0.00009933286,0.0003513082,0.0004151069,0.0001049841,0.0002302721],"domain_scores_gemma":[0.9993324,0.0002179328,0.00006692834,0.0002347946,0.00004618635,0.0001017747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003012872,0.00009097931,0.004042735,0.00005073987,0.0001015545,0.000005694134,0.0003963846,0.0001891775,0.009736009,0.00001127663,0.0002328076,0.9851125],"study_design_scores_gemma":[0.001991913,0.0006890059,0.2733876,0.0002293393,0.000048458,0.00004503872,0.0000284146,0.7187951,0.0001137366,0.001560891,0.002785262,0.000325191],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7652989,0.002630017,0.2315843,0.00005740207,0.00008219523,0.0002009616,0.00004221555,0.00008448499,0.00001953513],"genre_scores_gemma":[0.6346283,0.0004580796,0.3643609,0.000134619,0.0001644934,0.00000730214,0.0002279285,0.0000136449,0.000004682661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9847873,"threshold_uncertainty_score":0.6907301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1336602277721455,"score_gpt":0.3597837966646985,"score_spread":0.2261235688925529,"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."}}