{"id":"W2063094280","doi":"10.1118/1.4908000","title":"Automatic learning‐based beam angle selection for thoracic IMRT","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network; Philips (Canada); Princess Margaret Cancer Centre","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs; California HIV/AIDS Research Program","keywords":"Computer science; Beam (structure); Radiation treatment planning; Medical physics; Selection (genetic algorithm); Artificial intelligence; Radiation therapy; Medicine; Optics; Radiology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002314359,0.0001286535,0.0001844228,0.00002149122,0.00008414076,0.00001838757,0.0001309421,0.00005500718,0.0001988102],"category_scores_gemma":[0.00005673786,0.0001156953,0.00008922192,0.000167705,0.00006087847,0.0001049748,0.00001495797,0.0002290057,0.00001016728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005694912,"about_ca_system_score_gemma":0.0001824852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002180313,"about_ca_topic_score_gemma":4.543612e-7,"domain_scores_codex":[0.9990846,0.00003527177,0.0001524041,0.0001797558,0.0003227067,0.0002252922],"domain_scores_gemma":[0.9994128,0.0001124773,0.00008423356,0.0001202813,0.00009153481,0.000178672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005157087,0.0006781198,0.02754056,0.00007355201,0.0001081148,9.782458e-7,0.0003504245,0.001247373,0.000927278,0.003141976,0.01580172,0.9500783],"study_design_scores_gemma":[0.002652172,0.001098404,0.0002172441,0.0001790923,0.00009078265,0.000001929341,0.00009793521,0.5777142,0.211824,0.1068206,0.09847088,0.0008327251],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02494539,0.00002538475,0.9734298,0.0001731621,0.0001096104,0.0002600946,0.000004683911,0.0003101706,0.0007417301],"genre_scores_gemma":[0.9793025,7.503804e-7,0.01872706,0.0002156348,0.001184831,0.0001750509,0.00007346038,0.00004614882,0.0002745554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9547027,"threshold_uncertainty_score":0.4717916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01953909066135643,"score_gpt":0.3334301911809434,"score_spread":0.313891100519587,"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."}}