{"id":"W6907970757","doi":"10.25452/figshare.plus.24255795.v1","title":"Running Injury Clinic Kinematic Dataset","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kinematics; Motion capture; Motion (physics); Metadata; Data file; Motion analysis; Trajectory; Code (set theory); MATLAB","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007150665,0.00172426,0.001482941,0.002885034,0.0009519709,0.001383581,0.002458688,0.001340758,0.08370337],"category_scores_gemma":[0.003834638,0.0004529795,0.0009201979,0.003258433,0.0002923663,0.0008325755,0.001815254,0.0009547345,0.06297287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008158778,"about_ca_system_score_gemma":0.001748434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01659942,"about_ca_topic_score_gemma":0.04075582,"domain_scores_codex":[0.9991486,0.0001121679,0.0001458808,0.0002671647,0.0002258971,0.0001001482],"domain_scores_gemma":[0.9977907,0.0002594992,0.000230311,0.0004068507,0.001119633,0.00019303],"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":[0.0008377334,0.0003322265,0.01998152,0.001719796,0.0001477395,0.0002893507,0.0001289751,0.00053499,0.0005648847,0.0006033421,0.9190211,0.05583829],"study_design_scores_gemma":[0.0005014817,0.0003831178,0.1697961,0.001393409,0.0002016169,0.0009562908,0.0007651037,0.002116711,0.0009698298,0.00232966,0.8203909,0.0001958098],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005973138,0.000343571,0.001586113,0.0002206389,0.0001004512,0.000308965,0.9850897,0.001205301,0.005172185],"genre_scores_gemma":[0.006649475,0.0001817269,0.001485395,0.0001288244,0.00004499867,0.001067328,0.9865333,0.00008649501,0.003822518],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08370337,"threshold_uncertainty_score":0.2800156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1603573590535085,"score_gpt":0.4099337676588458,"score_spread":0.2495764086053373,"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."}}