{"id":"W2128323999","doi":"10.1142/s021951941350022x","title":"PREDICTION OF MUSCLE FORCES USING STATIC OPTIMIZATION FOR DIFFERENT CONTRACTILE CONDITIONS","year":2012,"lang":"en","type":"article","venue":"Journal of Mechanics in Medicine and Biology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Weighting; Function (biology); Computer science; Degrees of freedom (physics and chemistry); Biological system; Control theory (sociology); Simulation; Mathematics; Physics; Artificial intelligence; Biology","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":[0.0002062686,0.00004937183,0.0001842831,0.0001696215,0.00002157451,0.000001026596,0.00002159018,0.00004180697,0.00001520578],"category_scores_gemma":[0.00008311423,0.00003476982,0.0000222414,0.00006681748,0.00001727392,0.00007606322,0.000003308524,0.00006696877,3.652156e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001711238,"about_ca_system_score_gemma":0.000004088179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002435815,"about_ca_topic_score_gemma":0.000001586243,"domain_scores_codex":[0.9995593,0.00001853487,0.0002674944,0.00002693907,0.00003532851,0.00009235396],"domain_scores_gemma":[0.9996184,0.0001483144,0.0001194829,0.00002363216,0.00006334962,0.00002685369],"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":[0.0001755345,0.0003325648,0.01708856,0.0006711558,0.0006255711,9.013995e-7,0.003611361,0.03633003,0.8863727,0.02579954,0.002208994,0.02678306],"study_design_scores_gemma":[0.006868248,0.002913005,0.03759715,0.0007756056,0.0004280621,0.00008460377,0.006017616,0.9121312,0.01412783,0.01658288,0.002193067,0.0002806536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6884896,0.0008103952,0.3100857,0.0001110784,0.0003712201,0.00009058574,0.00001298458,0.000005187136,0.00002333682],"genre_scores_gemma":[0.9973671,0.001044851,0.001410836,0.00003341194,0.0001296465,0.000003092867,0.000006598719,0.000004060082,3.722247e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8758013,"threshold_uncertainty_score":0.1417872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0628234319452609,"score_gpt":0.3046223696981618,"score_spread":0.2417989377529008,"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."}}