{"id":"W2737396465","doi":"","title":"Analysis of Big Data in Running Biomechanics: Application of Multivariate Analysis and Machine Learning Methods","year":2016,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Bone fractures and treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Big data; Multivariate statistics; Computer science; Machine learning; Data collection; Gait analysis; Multivariate analysis; Data analysis; Data mining; Artificial intelligence; Analytics; Data science; Gait; Statistics; Mathematics","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":[0.0005638641,0.0001115156,0.0005812368,0.001005167,0.00002418103,0.000006387778,0.0001018958,0.00007305179,0.00001147576],"category_scores_gemma":[0.0001863301,0.00007163396,0.0000873382,0.002150248,0.00004050651,0.00009939352,0.000133659,0.00007164056,3.906872e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002549904,"about_ca_system_score_gemma":0.00001053784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100793,"about_ca_topic_score_gemma":0.00007574951,"domain_scores_codex":[0.999043,0.00001003452,0.0003369403,0.0003473587,0.0001398581,0.000122841],"domain_scores_gemma":[0.9992405,0.00007217869,0.0002980701,0.0002197332,0.0001148572,0.00005465929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000678921,0.00007828497,0.6726604,0.00005022142,0.002673717,5.868177e-7,0.0002335455,0.000002391798,0.1661188,0.00009293226,5.698805e-7,0.1580207],"study_design_scores_gemma":[0.001776783,0.0001611517,0.7782862,0.0001148793,0.01502854,0.000002555814,0.0001667825,0.1407693,0.06289729,0.0001483007,0.0005058867,0.0001423223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452636,0.0004458342,0.05379004,0.0001352412,0.00001196973,0.0001729228,0.00002884211,0.00002040663,0.0001311234],"genre_scores_gemma":[0.9827449,0.0001291579,0.0169307,0.00001152901,0.00001408586,0.000008829259,0.00009025154,0.00001048792,0.00006007935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1578784,"threshold_uncertainty_score":0.2921148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04752247546223819,"score_gpt":0.3599334826469934,"score_spread":0.3124110071847552,"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."}}