{"id":"W1984377284","doi":"10.1115/biomed2010-32057","title":"Tracking the 3D Configuration of Human Joint Using an MR Image Registration Technique","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Translation (biology); Computer vision; Rotation (mathematics); Artificial intelligence; Kinematics; Image registration; Transformation matrix; Point cloud; Computer science; Coordinate system; Rigid transformation; Transformation (genetics); Tracking (education); Point (geometry); Medical imaging; Image (mathematics); Mathematics; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155518,0.00005574634,0.00008155945,0.00004040026,0.0000614977,0.00003975662,0.00006321327,0.00004202508,0.0001917731],"category_scores_gemma":[0.00003659933,0.00003897927,0.00003130045,0.00007997501,0.00007035497,0.0001528832,0.000003557127,0.0001721698,0.000001520578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006912583,"about_ca_system_score_gemma":0.000007137249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001265518,"about_ca_topic_score_gemma":0.00004948743,"domain_scores_codex":[0.9995512,0.00001651535,0.0001865039,0.00006782302,0.0001053864,0.00007251132],"domain_scores_gemma":[0.9996951,0.00001095636,0.00003677183,0.0001822369,0.00004560258,0.0000293923],"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":[8.739086e-8,0.000007881921,0.00002945912,0.00001322887,0.000005579147,5.038553e-7,0.00008149576,0.0003127051,0.9962338,0.0007749727,0.00005662519,0.002483627],"study_design_scores_gemma":[0.00003709731,0.000005841337,0.0002121002,0.00001400622,0.00001979345,0.000004320883,0.00008294692,0.2041995,0.7950425,0.0002510053,0.00007476598,0.00005614068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3717607,0.000004126566,0.6203003,0.00007859816,0.00004529085,0.00006879641,6.025764e-7,0.0001255991,0.007615978],"genre_scores_gemma":[0.9899866,0.000001194786,0.009845721,0.00001709014,0.00007590235,0.000003857211,0.000008397053,0.000008498329,0.00005272364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6182259,"threshold_uncertainty_score":0.2099781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042758353585882,"score_gpt":0.285095203801661,"score_spread":0.2546676202658022,"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."}}