{"id":"W2154602866","doi":"10.1088/0031-9155/56/20/001","title":"Automated quantification of three-dimensional subject motion to monitor image quality in high-resolution peripheral quantitative computed tomography","year":2011,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Imaging phantom; Translation (biology); Image quality; Computer vision; Rotation (mathematics); Projection (relational algebra); Artificial intelligence; Computer science; Quantitative computed tomography; Image plane; Orientation (vector space); Biomedical engineering; Physics; Mathematics; Image (mathematics); Optics; Bone density; Osteoporosis; Algorithm; Geometry; Medicine","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":[0.0005018697,0.0001091397,0.000375242,0.0001837698,0.00002544727,0.00000135921,0.00006298047,0.00008129019,0.0000164573],"category_scores_gemma":[0.000126293,0.0000836082,0.00002875061,0.0005447522,0.000357863,0.00003877678,0.00003216472,0.0001648001,0.000001601491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000275324,"about_ca_system_score_gemma":0.00001907224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003411116,"about_ca_topic_score_gemma":0.0001093515,"domain_scores_codex":[0.998955,0.0001127056,0.0004216754,0.0002608579,0.00009773531,0.0001520166],"domain_scores_gemma":[0.9993706,0.000116689,0.0001214172,0.000177232,0.0001437171,0.00007030405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006334148,0.001163121,0.1939393,0.0002105313,0.00005311236,0.000005680339,0.001383607,0.0000264003,0.6954318,0.0934063,0.001057226,0.01268949],"study_design_scores_gemma":[0.001538354,0.0008610587,0.9190149,0.0003106604,0.00003352958,0.000004142705,0.0001518746,0.05189577,0.009685131,0.01634632,0.00002318005,0.0001350517],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9202265,0.00008392731,0.07710599,0.001929507,0.00006446723,0.0004176082,0.00001176757,0.00008873956,0.00007145025],"genre_scores_gemma":[0.9493923,0.00001750224,0.05011249,0.0002218127,0.00005825366,0.00004360858,0.0001458526,0.000006676535,0.000001567166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7250757,"threshold_uncertainty_score":0.515661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2096675446047079,"score_gpt":0.4278467614277371,"score_spread":0.2181792168230292,"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."}}