{"id":"W2175730465","doi":"10.1117/1.jmi.2.4.044002","title":"Quantitative performance characterization of image quality and radiation dose for a CS 9300 dental cone beam computed tomography machine","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Centre National de la Recherche Scientifique","keywords":"Imaging phantom; Cone beam computed tomography; Image quality; Medicine; Nuclear medicine; Pixel; Scanner; Image resolution; Dot pitch; Ionization chamber; Optics; Medical physics; Computed tomography; Physics; Artificial intelligence; Radiology; Ionization; Image (mathematics); Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002691083,0.0003220469,0.0002950635,0.001169635,0.0001865585,0.0007988749,0.0005069685,0.0005238306,0.002006943],"category_scores_gemma":[0.009109792,0.0003106981,0.0002661795,0.0006738295,0.0003652455,0.0004691269,0.0003682876,0.000273144,0.0002905437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005355122,"about_ca_system_score_gemma":0.0003640928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00225578,"about_ca_topic_score_gemma":0.001846054,"domain_scores_codex":[0.9984682,0.0002857762,0.0001144837,0.0002294409,0.0008266668,0.00007543627],"domain_scores_gemma":[0.9918625,0.004616252,0.0005961047,0.0005513755,0.002215875,0.000157725],"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":[0.00164362,0.0001550843,0.04076445,0.0003504517,0.0001110573,0.0001407096,0.0005605875,0.00627708,0.8788753,0.0003768191,0.0002950236,0.07044991],"study_design_scores_gemma":[0.00006072106,0.002930927,0.3029595,0.0000635198,0.0002859349,0.001845463,0.000272019,0.03615402,0.6525308,0.0001914877,0.002581658,0.000123892],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9398595,0.001192131,0.05552974,0.00007043767,0.00001086869,0.0001516904,0.0003798398,0.0004064568,0.002399184],"genre_scores_gemma":[0.9521416,0.0003688826,0.04556602,0.0001008539,0.00001053602,0.00007957548,0.0005758632,0.0002322003,0.0009244384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002691083,"threshold_uncertainty_score":0.01423204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02646565339722995,"score_gpt":0.3378828525717866,"score_spread":0.3114171991745566,"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."}}