{"id":"W3208594877","doi":"","title":"Validation of a Markerless Motion Capture System for Human Movement Analysis","year":2020,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion capture; Movement (music); Human motion; Motion (physics); Motion analysis; Computer vision; Artificial intelligence; Computer science; Art; Aesthetics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003212416,0.0002773123,0.0005604141,0.0008605411,0.0001031207,0.00005117203,0.000222625,0.0002808072,0.0002363369],"category_scores_gemma":[0.000004834078,0.0003411335,0.0005848381,0.001154581,0.00001183673,0.0003948616,0.00002155042,0.0001769534,0.000008623558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276801,"about_ca_system_score_gemma":0.0000310139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006491125,"about_ca_topic_score_gemma":0.00008137847,"domain_scores_codex":[0.9989679,0.00006082104,0.000249898,0.0003220255,0.000228398,0.0001709512],"domain_scores_gemma":[0.9993064,0.00002842475,0.0002343348,0.0002158073,0.0001000135,0.0001150659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002430561,0.001379096,0.06130822,0.08356514,0.08993781,0.0004394936,0.02349075,0.2665473,0.009982749,0.04811469,0.404256,0.008548195],"study_design_scores_gemma":[0.009897067,0.0004194757,0.07215547,0.004016893,0.08662473,4.409583e-7,0.2047489,0.05317084,0.4829317,0.001148054,0.07565976,0.009226698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8457966,0.00009869673,0.07061937,0.003954304,0.0009603676,0.002545116,0.00235883,0.002855028,0.07081165],"genre_scores_gemma":[0.9548966,0.00005127233,0.0007075774,0.00001294554,0.00004905125,0.000004860191,0.01497825,0.00005512482,0.02924428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4729489,"threshold_uncertainty_score":0.9999041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005895814010458696,"score_gpt":0.1790156894496366,"score_spread":0.1731198754391779,"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."}}