{"id":"W2054604301","doi":"10.1118/1.2240248","title":"SU‐FF‐I‐10: Compensators for Management of Dose and Scatter in Cone‐Beam CT","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Imaging phantom; Flat panel detector; Cone beam computed tomography; Optics; Detector; Magnification; Beam (structure); Medical imaging; Image quality; Cone beam ct; Nuclear medicine; Image-guided radiation therapy; Range (aeronautics); Materials science; Physics; Computed tomography; Medicine; Radiology; Computer science","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":[0.00115989,0.0007980042,0.0003164918,0.0007117806,0.0001796912,0.0005076849,0.0009614382,0.0005007168,0.001029125],"category_scores_gemma":[0.002244412,0.0004156219,0.0002451751,0.0005714219,0.0004005112,0.0004285506,0.0002346776,0.0004020624,0.0004581141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000584032,"about_ca_system_score_gemma":0.0005355189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292785,"about_ca_topic_score_gemma":0.002065131,"domain_scores_codex":[0.9994171,0.0001050469,0.00003736294,0.0001426072,0.0002324618,0.00006546121],"domain_scores_gemma":[0.999082,0.000304517,0.0002530802,0.0001359474,0.0001663312,0.00005805781],"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":[0.0008999369,0.0001045313,0.005858378,0.0003156838,0.00004389669,0.00009568024,0.00008350665,0.002593064,0.9128464,0.0003194273,0.0008401962,0.07599935],"study_design_scores_gemma":[0.00006732483,0.001301509,0.02593638,0.00005482811,0.000146374,0.00125383,0.00003928502,0.01987315,0.941076,0.0001267397,0.01006798,0.00005661674],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7382667,0.01022746,0.2439017,0.0002385769,0.000166769,0.000287701,0.0004441158,0.002818146,0.003648839],"genre_scores_gemma":[0.8323049,0.001936611,0.1601877,0.0001429704,0.00003854214,0.0001700898,0.0007971444,0.00037978,0.004042402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001292785,"threshold_uncertainty_score":0.006134152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007095884490594116,"score_gpt":0.2310849395349767,"score_spread":0.2239890550443826,"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."}}