{"id":"W2101889219","doi":"10.1504/ijecb.2009.022862","title":"Precision, repeatability and accuracy of Optotrak<SUP align=right>®</SUP> optical motion tracking systems","year":2009,"lang":"en","type":"article","venue":"International Journal of Experimental and Computational Biomechanics","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston General Hospital; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Minnesota; University of Wisconsin-Madison","keywords":"Repeatability; Tracking (education); Displacement (psychology); Computer vision; Tilt (camera); Accuracy and precision; Match moving; Motion (physics); Artificial intelligence; Computer science; Biomedical engineering; Materials science; Mathematics; Engineering; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005905297,0.0004983394,0.0004135559,0.0009224258,0.0003587134,0.000806273,0.0004917849,0.0006174474,0.0007729841],"category_scores_gemma":[0.01933619,0.0004637294,0.0003613021,0.0006495109,0.0006080574,0.0006137149,0.0006986154,0.0003529229,0.0003763254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003572822,"about_ca_system_score_gemma":0.0005523923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001428916,"about_ca_topic_score_gemma":0.002771034,"domain_scores_codex":[0.9934751,0.001631777,0.0007143721,0.001256999,0.002753748,0.0001679655],"domain_scores_gemma":[0.9871297,0.005893187,0.001409636,0.00146883,0.003964414,0.0001341141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002618398,0.0002828557,0.05704328,0.0009066402,0.0003779621,0.0001237966,0.001502063,0.005899435,0.6471426,0.0007721957,0.001013075,0.2823178],"study_design_scores_gemma":[0.0002487574,0.006333687,0.3701309,0.0002104673,0.0009338848,0.001469217,0.0006806667,0.02796589,0.5807742,0.001035608,0.009867835,0.0003489067],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8442931,0.003458349,0.1468811,0.0001781327,0.0002926412,0.0003145895,0.000461786,0.0007074799,0.003412742],"genre_scores_gemma":[0.9392473,0.0005172031,0.0580631,0.00006330475,0.00004123147,0.0001464348,0.000215233,0.0001255528,0.001580634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005905297,"threshold_uncertainty_score":0.03123057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148585941891479,"score_gpt":0.3328696701626577,"score_spread":0.3180110759735097,"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."}}