{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001039648,0.0006184074,0.0005623019,0.000788388,0.0003053617,0.0004375321,0.0008501104,0.0004091671,0.001331188],"category_scores_gemma":[0.00255216,0.0003674093,0.0004672782,0.0006311947,0.0002785597,0.0003988086,0.0004115955,0.0005781477,0.0004244272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006703256,"about_ca_system_score_gemma":0.0006999943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004407976,"about_ca_topic_score_gemma":0.00679645,"domain_scores_codex":[0.9993175,0.0002743269,0.00003473472,0.000118529,0.0002086625,0.00004607698],"domain_scores_gemma":[0.9990645,0.0004898544,0.0001548965,0.0001077932,0.0001605264,0.00002235707],"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":[0.0001813824,0.000126031,0.005336145,0.00009212828,0.00005153154,0.00004290267,0.00005864503,0.4440745,0.01627776,0.0007377531,0.001464424,0.5315568],"study_design_scores_gemma":[0.00001783221,0.0000505835,0.001848286,0.00000684042,0.000009199368,0.00003525113,0.000007217445,0.9885314,0.008132351,0.0006612239,0.0006903808,0.000009567069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07374529,0.0004762877,0.9220659,0.00006693236,0.00001680568,0.0001305552,0.0001310353,0.002435231,0.0009318806],"genre_scores_gemma":[0.5750551,0.000146594,0.4231162,0.00005080658,0.0000216747,0.0001326645,0.0004590161,0.0002025482,0.0008154843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004407976,"threshold_uncertainty_score":0.008764625,"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."}}