{"id":"W4394407023","doi":"10.6084/m9.figshare.7869293","title":"An optimization method tracking EMG, ground reactions forces and marker trajectories for musculo-tendon forces estimation in equinus gait","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg","keywords":"Ground reaction force; Gait; Gait analysis; Physical medicine and rehabilitation; Tracking (education); Tendon; Computer science; Medicine; Anatomy; Physics; Psychology; Kinematics; Classical mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001411747,0.004383803,0.001824877,0.002278814,0.0008203541,0.001287526,0.003666224,0.002971627,0.01477412],"category_scores_gemma":[0.003679892,0.0007210878,0.002370862,0.002481558,0.0005654433,0.0005881899,0.002169136,0.001373911,0.02134906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008261439,"about_ca_system_score_gemma":0.001321652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0258438,"about_ca_topic_score_gemma":0.061543,"domain_scores_codex":[0.9989457,0.0002043516,0.0001353007,0.0003451141,0.0002596792,0.0001098584],"domain_scores_gemma":[0.9987816,0.0003516556,0.00009172031,0.0004082903,0.0002913426,0.00007538464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007946152,0.0004683106,0.003299746,0.002596084,0.000468467,0.000186915,0.00006655931,0.01066593,0.002939122,0.0005810134,0.9219493,0.05598389],"study_design_scores_gemma":[0.003146818,0.0009728262,0.05520332,0.001275786,0.0007728066,0.001256016,0.0003378308,0.1009055,0.01480399,0.007061877,0.8138511,0.0004122078],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008181238,0.0008801108,0.006373447,0.0002563684,0.0002780771,0.0002531208,0.9736294,0.008220766,0.001927539],"genre_scores_gemma":[0.005777705,0.0001126188,0.006189575,0.00006089094,0.0000186302,0.0003823071,0.9857758,0.0002227968,0.001459649],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0258438,"threshold_uncertainty_score":0.05138671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.034561907181145,"score_gpt":0.3012152824727667,"score_spread":0.2666533752916218,"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."}}