{"id":"W6945589161","doi":"10.25452/figshare.plus.24255795","title":"Running Injury Clinic Kinematic Dataset","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kinematics; Motion capture; Data file; Metadata; Motion (physics); Code (set theory); Trajectory; STRIDE","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":[0.0007059678,0.001749732,0.001549805,0.002983769,0.0008770549,0.001420476,0.00252893,0.001462076,0.07783471],"category_scores_gemma":[0.004098465,0.0004536307,0.001060514,0.003705437,0.0003114129,0.0008596695,0.001843014,0.001040877,0.06897655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008398123,"about_ca_system_score_gemma":0.001821164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01770477,"about_ca_topic_score_gemma":0.03916564,"domain_scores_codex":[0.9990521,0.000121079,0.0001597307,0.000313511,0.0002433328,0.0001102755],"domain_scores_gemma":[0.9978119,0.000266553,0.0002271716,0.0004067792,0.001108818,0.0001786626],"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":[0.0007398145,0.0002840006,0.01806576,0.001587094,0.0001426026,0.0002412853,0.0001073404,0.0006882545,0.0005743966,0.0005973597,0.9309763,0.04599584],"study_design_scores_gemma":[0.0003875589,0.0002988791,0.1215021,0.001142905,0.0001706335,0.0007068051,0.000617071,0.002066867,0.001041961,0.002188559,0.8697008,0.0001758337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003498996,0.000263689,0.001127101,0.0001627875,0.00007890739,0.0001799319,0.9904193,0.0009727867,0.003296579],"genre_scores_gemma":[0.003768157,0.0001335044,0.001097165,0.00009913001,0.00002671055,0.0006204984,0.9918867,0.00006833702,0.002299805],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07783471,"threshold_uncertainty_score":0.2603831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03510341692301775,"score_gpt":0.314962707576501,"score_spread":0.2798592906534833,"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."}}