{"id":"W2052848954","doi":"10.1118/1.4888009","title":"SU‐E‐I‐59: Image Quality and Dose Measurement for Partial Cone‐Beam CT","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image quality; Imaging phantom; Cone beam computed tomography; Nuclear medicine; Contrast-to-noise ratio; Materials science; Ionization chamber; Image noise; Dosimetry; Optics; Voxel; Physics; Biomedical engineering; Medicine; Computed tomography; Image (mathematics); Ionization; Computer science; Radiology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002395426,0.0004675303,0.0004682928,0.000973311,0.0002148873,0.0007078936,0.0006634381,0.0006581236,0.001338617],"category_scores_gemma":[0.004934472,0.0004409952,0.0003830616,0.0005896608,0.0004940086,0.0004502241,0.0003293238,0.000424374,0.0003690636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005263654,"about_ca_system_score_gemma":0.0003391359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878366,"about_ca_topic_score_gemma":0.002176707,"domain_scores_codex":[0.9981747,0.0003378787,0.0001194947,0.0003190067,0.0009729628,0.00007604204],"domain_scores_gemma":[0.998054,0.000671868,0.0003649974,0.0002310953,0.000599526,0.00007846435],"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":[0.00130979,0.0001267926,0.01771062,0.0003007493,0.00006453333,0.0001155934,0.00009930444,0.001537833,0.9230515,0.0001550596,0.0003642884,0.05516393],"study_design_scores_gemma":[0.00005453206,0.002053534,0.1640792,0.00004784887,0.0001595719,0.002372555,0.00005455967,0.02461086,0.803945,0.00008751625,0.002459404,0.000075482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531824,0.004401891,0.1361478,0.0001276,0.00004956381,0.0002806402,0.0006407098,0.001196645,0.003972874],"genre_scores_gemma":[0.887933,0.0009589354,0.1081246,0.00009578346,0.00001935332,0.0001201061,0.001010733,0.000334488,0.001402912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002395426,"threshold_uncertainty_score":0.01266837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04709638571651677,"score_gpt":0.330145315814034,"score_spread":0.2830489300975172,"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."}}