{"id":"W2242621671","doi":"","title":"Development of a calibration procedure for integration of dual fluoroscopy and motion analysis","year":2013,"lang":"en","type":"article","venue":"Journal of undergraduate research in Alberta","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Kinematics; Computer science; Artifact (error); Calibration; Computer vision; Motion capture; Artificial intelligence; Fluoroscopy; Gait analysis; Gait; Joint (building); Frame (networking); Motion analysis; Motion (physics); Simulation; Engineering; Mathematics; Medicine; Physics; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.002442271,0.001108136,0.0006721158,0.001928407,0.0004908485,0.000834043,0.001825247,0.001234489,0.007005938],"category_scores_gemma":[0.005806396,0.0008635247,0.0005467039,0.001044098,0.0004799446,0.0008400172,0.001176858,0.0009884881,0.003405693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004968868,"about_ca_system_score_gemma":0.001186698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001016841,"about_ca_topic_score_gemma":0.001152122,"domain_scores_codex":[0.9975788,0.0002353361,0.000163245,0.0006264196,0.001274256,0.0001219395],"domain_scores_gemma":[0.9967819,0.0006951581,0.0003477934,0.0006481399,0.001440416,0.00008650772],"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":[0.0002812476,0.0001637275,0.005189553,0.0004564579,0.00007964043,0.0002616612,0.0002888695,0.00475135,0.5222465,0.004396649,0.003015889,0.4588684],"study_design_scores_gemma":[0.0000580861,0.0007266156,0.01435472,0.0001084919,0.0001212404,0.001685145,0.00009038235,0.09425187,0.8236302,0.001402468,0.06338137,0.0001894803],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00848236,0.0001244265,0.9869485,0.00003729436,0.00006439845,0.0002399626,0.000119215,0.003253447,0.0007304958],"genre_scores_gemma":[0.05279904,0.0001059055,0.9445375,0.00005604279,0.00001937062,0.0003707038,0.0002255786,0.0004142333,0.001471686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007005938,"threshold_uncertainty_score":0.02343714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07862011328700753,"score_gpt":0.4035705801988458,"score_spread":0.3249504669118383,"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."}}