{"id":"W1963815462","doi":"10.1118/1.2740466","title":"Compensators for dose and scatter management in cone‐beam computed tomography","year":2007,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Institutes of Health","keywords":"Cone beam computed tomography; Detector; Beam (structure); Optics; Image-guided radiation therapy; Medical imaging; Cone beam ct; Image quality; Nuclear medicine; Medical physics; Computed tomography; Physics; Medicine; Computer science; Radiology; Artificial intelligence; Image (mathematics)","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":[0.0004454945,0.0001027336,0.000218596,0.00007153347,0.00003867469,0.000007846314,0.00009042746,0.00007189155,0.00003265411],"category_scores_gemma":[0.00002908323,0.00008581154,0.00005555422,0.0002645253,0.0002311876,0.00002131353,0.00006336028,0.00020363,0.000005283935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002181052,"about_ca_system_score_gemma":0.00001911914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001012472,"about_ca_topic_score_gemma":0.000002308398,"domain_scores_codex":[0.9989522,0.000006771284,0.0002471819,0.0002191908,0.0003295458,0.0002451251],"domain_scores_gemma":[0.9992935,0.0001403517,0.00003522219,0.0001906749,0.00003444426,0.0003057873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000254204,0.002433734,0.05710194,0.001028288,0.0002217891,0.0002349808,0.0002962743,6.490981e-7,0.001325161,0.04882915,0.1009684,0.7873055],"study_design_scores_gemma":[0.02284,0.0008757769,0.5998327,0.002292835,0.0005241394,0.000110838,0.0002737571,0.01291701,0.02374318,0.06553161,0.2697621,0.001296048],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4608559,0.0001181727,0.5147863,0.0180944,0.0001508416,0.00191011,0.000008094573,0.0002718193,0.003804357],"genre_scores_gemma":[0.9772112,0.00002680092,0.01483519,0.007520056,0.0002287563,0.00006473014,0.0000474798,0.00001580403,0.00004999358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7860094,"threshold_uncertainty_score":0.3499293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107799322283754,"score_gpt":0.3224874437620956,"score_spread":0.3014094505392581,"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."}}