{"id":"W2080458627","doi":"10.1118/1.2143141","title":"The stability of mechanical calibration for a kV cone beam computed tomography system integrated with linear acceleratora)","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; St. Michael's Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Radiation Oncology Institute","keywords":"Cone beam computed tomography; Calibration; Computed tomography; Beam (structure); Medical imaging; Tomography; Optics; Physics; Nuclear medicine; Medical physics; Medicine; Radiology","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":[0.002585933,0.0003861514,0.0002754395,0.0005777717,0.0003597785,0.0007827108,0.0005654023,0.0003941157,0.002397499],"category_scores_gemma":[0.008987203,0.0004309159,0.0001984364,0.0004948091,0.0004179825,0.0003845488,0.0006038886,0.0004126785,0.0005533991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006823576,"about_ca_system_score_gemma":0.0009927157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00164137,"about_ca_topic_score_gemma":0.001632938,"domain_scores_codex":[0.9980071,0.0002527146,0.0001065364,0.0003421577,0.00120291,0.00008863253],"domain_scores_gemma":[0.9953608,0.001422106,0.0009488088,0.0009351078,0.001231673,0.0001015883],"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":[0.000962379,0.0001805014,0.02202465,0.0001871214,0.00006271042,0.00007272733,0.0003210771,0.006610828,0.8768561,0.0006386457,0.0006145455,0.09146866],"study_design_scores_gemma":[0.0001032014,0.003173469,0.1799494,0.00004256759,0.0001291414,0.00116148,0.00009523106,0.02937551,0.7749848,0.0002752982,0.01061432,0.00009552549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7965252,0.001697108,0.1935996,0.0002240261,0.0001299233,0.0004318652,0.0004609456,0.001989138,0.004942233],"genre_scores_gemma":[0.9540616,0.0001519736,0.0431593,0.00007283009,0.00001716856,0.0001165462,0.0003347784,0.0002120264,0.001873595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002585933,"threshold_uncertainty_score":0.01367587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806658677492277,"score_gpt":0.2965424292515479,"score_spread":0.2684758424766251,"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."}}