{"id":"W2906336674","doi":"10.1002/mp.13896","title":"Knowledge‐based automated planning with three‐dimensional generative adversarial networks","year":2019,"lang":"en","type":"preprint","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; York University; University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pipeline (software); Scaling; Artificial neural network; Radiation treatment planning; Artificial intelligence; Mathematics; Medicine; Radiology; Geometry","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":[0.0008357554,0.0009766415,0.0005258853,0.0004582286,0.0002443817,0.0007475431,0.00134327,0.0008915837,0.002433838],"category_scores_gemma":[0.002693182,0.0006809183,0.0008736544,0.0003839299,0.0007838761,0.0008789209,0.0009768514,0.001627654,0.0005164332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001610247,"about_ca_system_score_gemma":0.001543047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01077903,"about_ca_topic_score_gemma":0.0105483,"domain_scores_codex":[0.9994884,0.0001253996,0.00002157872,0.0001247973,0.0001936901,0.00004609941],"domain_scores_gemma":[0.9987999,0.0007765367,0.0001192631,0.0001504474,0.0001133482,0.00004042827],"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":[0.00002262209,0.00001294291,0.0001358463,0.00002146209,0.00001313922,0.00001954022,0.0000134278,0.9760429,0.0008478037,0.001124874,0.0005775024,0.02116803],"study_design_scores_gemma":[0.000002992608,0.000006967936,0.00003448038,0.000003213246,0.000002384098,0.000008122992,0.000001733241,0.9976794,0.0007389028,0.001259439,0.0002593658,0.000003052025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01291098,0.0002056114,0.9816186,0.0002234983,0.00002066419,0.00007450159,0.0001785974,0.002264368,0.00250333],"genre_scores_gemma":[0.6217116,0.0002460909,0.3731727,0.0003950118,0.00003578226,0.0002718838,0.000640326,0.0005631471,0.00296354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01077903,"threshold_uncertainty_score":0.02143258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740467446030852,"score_gpt":0.3077374817338492,"score_spread":0.2903328072735407,"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."}}