{"id":"W1973004369","doi":"10.1177/0018720810370340","title":"Reliability of Distal Upper Extremity Posture Matching Using Slow-Motion and Frame-by-Frame Video Methods","year":2010,"lang":"en","type":"article","venue":"Human Factors The Journal of the Human Factors and Ergonomics Society","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Frame (networking); Reliability (semiconductor); Matching (statistics); Computer science; Motion (physics); Computer vision; Poison control; Physical medicine and rehabilitation; Artificial intelligence; Simulation; Medicine; Mathematics; Medical emergency; 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":[0.009461662,0.000635916,0.0005227131,0.001478338,0.0002785048,0.000674339,0.000654603,0.0004943664,0.00171258],"category_scores_gemma":[0.03833592,0.0002774632,0.0004784696,0.0006108205,0.0004146492,0.0006115195,0.0008878257,0.0002591649,0.0005050256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002748435,"about_ca_system_score_gemma":0.0003908216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001755182,"about_ca_topic_score_gemma":0.003031668,"domain_scores_codex":[0.990243,0.004453217,0.0009672084,0.001381588,0.002744751,0.0002102747],"domain_scores_gemma":[0.9694353,0.01310369,0.005132135,0.001886792,0.009997591,0.0004444518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006491324,0.0004804203,0.5037805,0.001601412,0.0005810963,0.0002342563,0.003647677,0.002302938,0.08422926,0.0002530281,0.001046022,0.3953519],"study_design_scores_gemma":[0.0001398224,0.00432796,0.94906,0.0002919654,0.0003691058,0.001069336,0.001441159,0.01541343,0.02578986,0.0003135324,0.001667197,0.0001167971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9381052,0.001117043,0.05758427,0.00004291019,0.0001246935,0.0005175602,0.0003901862,0.000259698,0.00185839],"genre_scores_gemma":[0.9562438,0.0004079295,0.04180499,0.00003636628,0.00006116592,0.0003732113,0.0003076438,0.00004397217,0.0007209846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009461662,"threshold_uncertainty_score":0.05003864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774617829222409,"score_gpt":0.3087708976902283,"score_spread":0.2910247193980042,"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."}}