{"id":"W4289801267","doi":"","title":"Ability of eight multi-joint models to compensate for soft tissue artefacts using global optimisation - An in vivo study of knee kinematics during squats","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"","keywords":"Kinematics; Joint (building); Computer science; Knee Joint; Soft tissue; Biomechanics; Biomedical engineering; Engineering; Anatomy; Physics; Structural engineering; Medicine; Surgery","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":[0.0009590645,0.0007353794,0.0006556957,0.0003925545,0.0002099838,0.00063447,0.000451082,0.0008067141,0.001083261],"category_scores_gemma":[0.002860802,0.0005167991,0.0006324475,0.0003266453,0.0003803056,0.0004542984,0.0005494381,0.0003577871,0.0002646534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001862062,"about_ca_system_score_gemma":0.0003515755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002651725,"about_ca_topic_score_gemma":0.002970581,"domain_scores_codex":[0.9997259,0.00009966204,0.00001980719,0.00006222587,0.00006445807,0.00002800624],"domain_scores_gemma":[0.9991254,0.0005158368,0.0001090662,0.0001103156,0.00009728943,0.00004207275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003763996,0.000493501,0.0115329,0.00049478,0.0003854831,0.0002712016,0.001291021,0.479821,0.3190685,0.0004848603,0.0004904646,0.1819023],"study_design_scores_gemma":[0.0001047988,0.001279698,0.04040775,0.00004061667,0.0002091983,0.0004743177,0.0002285273,0.9189871,0.03696871,0.0003888618,0.0008359455,0.00007447543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.846431,0.0002581142,0.1515762,0.00009142853,0.00003049177,0.00005193993,0.00015037,0.0004160005,0.0009945264],"genre_scores_gemma":[0.9759274,0.00007678915,0.02271994,0.00001069269,0.000003750728,0.0000218172,0.000153657,0.0001744833,0.0009114738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002651725,"threshold_uncertainty_score":0.005272627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07860289520054076,"score_gpt":0.3333657927079656,"score_spread":0.2547628975074248,"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."}}