{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007844108,0.00007870185,0.000340031,0.0007685869,0.00003973841,0.00001690758,0.00004388032,0.0000645812,0.000007694412],"category_scores_gemma":[0.0003238187,0.00005525525,0.00008686123,0.0006458982,0.00006395698,0.0002132354,0.00001777763,0.0001677349,5.386437e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159475,"about_ca_system_score_gemma":0.0003175706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003520739,"about_ca_topic_score_gemma":0.0006939688,"domain_scores_codex":[0.9985352,0.00009210163,0.0006336895,0.0001114942,0.0004757062,0.0001517789],"domain_scores_gemma":[0.9986593,0.0003099054,0.0002536951,0.00009520241,0.0005857261,0.00009617709],"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":[0.004625397,0.004677879,0.1745594,0.00267369,0.005587615,0.00002603243,0.007406648,0.0003401708,0.6716844,0.01918737,0.001566489,0.1076648],"study_design_scores_gemma":[0.006266224,0.00342917,0.08199041,0.001017061,0.001050762,0.00008760257,0.001610038,0.02533132,0.837921,0.04098168,0.0001286217,0.0001860659],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980113,0.0001062345,0.003868785,0.01523459,0.00002203205,0.0005790482,0.000001562319,0.000001214294,0.00007354437],"genre_scores_gemma":[0.9861358,0.00009059294,0.01351037,0.000008777395,0.0000242399,0.00002998979,0.00001779514,0.000007145372,0.000175296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1662366,"threshold_uncertainty_score":0.2253244,"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."}}