{"id":"W1828708358","doi":"10.1118/1.4930060","title":"Increasing efficiency of BEAMnrc‐simulated Co‐60 beams using directional source biasing","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fluence; Photon; Monte Carlo method; Physics; Electron; Optics; Imaging phantom; Dosimetry; Biasing; Beam (structure); Computational physics; Atomic physics; Nuclear physics; Voltage; Nuclear medicine; Mathematics; Statistics","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":[0.0006504349,0.0003529588,0.0003221354,0.0003549706,0.0002618049,0.0004885005,0.000658075,0.0004663561,0.002353684],"category_scores_gemma":[0.001765992,0.0002914998,0.0002841997,0.0005308914,0.0002253265,0.0003331561,0.0003496365,0.0002983029,0.0003847712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850634,"about_ca_system_score_gemma":0.001141809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009358848,"about_ca_topic_score_gemma":0.00748103,"domain_scores_codex":[0.9997944,0.00003919259,0.00001105692,0.00002534674,0.0001035235,0.00002636589],"domain_scores_gemma":[0.999234,0.0004112147,0.00006591066,0.00005894619,0.0002046245,0.00002539365],"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":[0.0003354629,0.00008608008,0.00792647,0.0002073299,0.00004303272,0.00005276865,0.0001763023,0.9295059,0.03523539,0.001237084,0.0008682073,0.02432588],"study_design_scores_gemma":[0.00004984195,0.00008202015,0.001983946,0.00001581493,0.00002127841,0.00003173061,0.0000256115,0.9451074,0.05058813,0.0002780899,0.001800084,0.00001602232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603331,0.0004521794,0.1200329,0.0002582938,0.00004361997,0.0001721543,0.0009470123,0.002183888,0.01557674],"genre_scores_gemma":[0.958563,0.0001368284,0.03888169,0.00006760263,0.000003579004,0.0001172306,0.0003777421,0.0004593676,0.001393016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009358848,"threshold_uncertainty_score":0.01860875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621197380753267,"score_gpt":0.3203161759087865,"score_spread":0.2941042021012538,"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."}}