{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004070976,0.0001691115,0.0003522263,0.0002470705,0.00005892024,0.0001018275,0.0001484099,0.00008774903,0.00003304784],"category_scores_gemma":[0.0001697692,0.0001374543,0.0001169592,0.0001055818,0.0001035144,0.0003434154,0.00005795329,0.0001663782,9.840348e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370271,"about_ca_system_score_gemma":0.00006584865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004847209,"about_ca_topic_score_gemma":7.035382e-8,"domain_scores_codex":[0.9980152,0.00004814492,0.000765915,0.0002356621,0.000794128,0.0001409557],"domain_scores_gemma":[0.9984998,0.0001944419,0.0003164644,0.00009484317,0.0007254056,0.0001690543],"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":[0.001110394,0.001712792,0.0003257508,0.00004704684,0.000279235,0.0001340254,0.0005559368,0.0003623687,0.881999,0.07579155,0.0003324911,0.03734946],"study_design_scores_gemma":[0.004150231,0.004194566,0.005385873,0.001090606,0.0001511835,0.005246888,0.0005339835,0.233159,0.7085134,0.03617574,0.0009549136,0.0004436169],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9125633,0.001986726,0.08290935,0.00155919,0.0002843025,0.0002279547,0.00002682017,0.0000345423,0.0004077942],"genre_scores_gemma":[0.9755967,0.0001194826,0.02387777,0.0001603183,0.0001863977,0.000002512259,0.00002868371,0.000009683606,0.00001847479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2327966,"threshold_uncertainty_score":0.5605225,"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."}}