{"id":"W2142708001","doi":"10.1118/1.2335490","title":"Evaluation of two methods of predicting MLC leaf positions using EPID measurements","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Cancer Research","keywords":"Multileaf collimator; Calibration; Position (finance); Extrapolation; Collimator; Collimated light; Image-guided radiation therapy; Mathematics; Interpolation (computer graphics); Optics; Computer science; Beam (structure); Physics; Computer vision; Artificial intelligence; Linear particle accelerator; Medical imaging; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004135109,0.001590727,0.0008686508,0.002301128,0.000338742,0.0008216054,0.001590682,0.00104202,0.001007006],"category_scores_gemma":[0.01610154,0.0005290562,0.0005861284,0.001135775,0.0002439024,0.001133261,0.0009205478,0.0007110823,0.0004242876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000628989,"about_ca_system_score_gemma":0.0009165456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442095,"about_ca_topic_score_gemma":0.002883127,"domain_scores_codex":[0.9970363,0.0008712765,0.0002181502,0.0004960626,0.001308385,0.0000699237],"domain_scores_gemma":[0.985046,0.007609082,0.001466113,0.001274882,0.004408007,0.0001960635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002759976,0.0008354333,0.111044,0.001138764,0.0005605526,0.0002214169,0.0006308187,0.1613185,0.07461368,0.001518337,0.002093994,0.6432646],"study_design_scores_gemma":[0.0002455152,0.001864023,0.05824088,0.0001412664,0.0002733688,0.0003981891,0.0001657169,0.8246742,0.1099378,0.0004829977,0.003350185,0.0002258419],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2486402,0.000927767,0.7430426,0.000135409,0.0001664235,0.0004394895,0.0007771947,0.003592198,0.002278676],"genre_scores_gemma":[0.5932757,0.0004980846,0.403314,0.00006618815,0.00004970319,0.0004125499,0.001019571,0.0003163893,0.001047829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004135109,"threshold_uncertainty_score":0.02186877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09162460876806418,"score_gpt":0.4433520944909106,"score_spread":0.3517274857228464,"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."}}