{"id":"W2047582893","doi":"10.1118/1.4829513","title":"Compensator models for fluence field modulated computed tomography","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Image quality; Fluence; Computer science; Medical imaging; Quality assurance; Simulated annealing; Medical physics; Artificial intelligence; Computer vision; Nuclear medicine; Image (mathematics); Algorithm; Optics; Physics; Medicine","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":[0.0003113191,0.0004085476,0.0002498144,0.0003517133,0.0002001251,0.0003814751,0.0008077545,0.0009677219,0.00295938],"category_scores_gemma":[0.001447845,0.0002447601,0.0004817209,0.00022171,0.0003354648,0.0003477489,0.0003663989,0.0004411894,0.0005749865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008106306,"about_ca_system_score_gemma":0.0006252078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005686363,"about_ca_topic_score_gemma":0.005281779,"domain_scores_codex":[0.9998635,0.00003550265,0.000004996213,0.00002383883,0.00005823197,0.00001380313],"domain_scores_gemma":[0.9997169,0.0001527018,0.00005479124,0.00002189942,0.00004245438,0.00001123763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002946425,0.00001080923,0.0003343141,0.00003817446,0.00001378017,0.00005753655,0.00004319179,0.9803348,0.005889375,0.006432469,0.0003504765,0.006465571],"study_design_scores_gemma":[0.000006702579,0.00001593893,0.0001859011,0.000007464855,0.000005309053,0.00003074697,0.000004060093,0.9954292,0.00109194,0.001415759,0.001800053,0.000007004048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02482005,0.0004626013,0.9655783,0.0002090701,0.00004503869,0.0001114341,0.0001649206,0.0005615085,0.008047053],"genre_scores_gemma":[0.8489076,0.0006227593,0.1318503,0.0002043801,0.0000346343,0.0004625064,0.0003904745,0.000425018,0.01710216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005686363,"threshold_uncertainty_score":0.01130652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850091557189386,"score_gpt":0.269992248699003,"score_spread":0.2514913331271091,"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."}}