{"id":"W4205706394","doi":"10.51224/srxiv.101","title":"Validation of PITCHAI Markerless Motion Capture Using Gold Standard 3D Motion Capture","year":2022,"lang":"en","type":"preprint","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Motion capture; Gold standard (test); Motion (physics); Computer vision; Automatic identification and data capture; Artificial intelligence; Computer science; Computer graphics (images); Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003326351,0.00136983,0.0006966455,0.00188312,0.0006435132,0.002363966,0.001688262,0.002317182,0.004753368],"category_scores_gemma":[0.009131779,0.0004932454,0.0007185494,0.0008969341,0.0009794394,0.001206202,0.002441277,0.0006957024,0.003909712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004764067,"about_ca_system_score_gemma":0.0007826306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003519971,"about_ca_topic_score_gemma":0.004695327,"domain_scores_codex":[0.9965565,0.0005116487,0.0002057888,0.0009430883,0.001492841,0.0002901949],"domain_scores_gemma":[0.9960095,0.0008931278,0.0002598802,0.001284001,0.001410286,0.0001431317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002408762,0.000991269,0.0476384,0.00177552,0.0009212258,0.0008120913,0.0008789195,0.1298413,0.3089636,0.005179144,0.0148253,0.4857646],"study_design_scores_gemma":[0.0001827413,0.001548824,0.1181516,0.0003756419,0.0002614357,0.002102189,0.0006412421,0.618822,0.2347193,0.002161279,0.02086768,0.0001660676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4002239,0.001120476,0.5715016,0.0003283069,0.000763167,0.0008560701,0.006429772,0.009278141,0.009498619],"genre_scores_gemma":[0.8325294,0.0004643263,0.140731,0.0003222757,0.00006532871,0.0004068961,0.01736119,0.0007949821,0.0073247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004753368,"threshold_uncertainty_score":0.01759166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924586441143596,"score_gpt":0.2401829087261255,"score_spread":0.2209370443146895,"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."}}