{"id":"W2944684921","doi":"10.1088/1361-6560/ab1beb","title":"Corrigendum: A mixed-integer linear programming optimization model framework for capturing expert planning style in low dose rate prostate brachytherapy (2019 <i>Phys. Med. Biol</i> . <b>64</b> 075007)","year":2019,"lang":"en","type":"erratum","venue":"Physics in Medicine and Biology","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Brachytherapy; Integer programming; Dose rate; Prostate brachytherapy; Radiation treatment planning; Integer (computer science); Linear programming; Prostate; Computer science; Medical physics; Nuclear medicine; Medicine; Mathematical optimization; Mathematics; Algorithm; Radiology; Internal medicine; Radiation therapy; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003341431,0.0005891374,0.001468722,0.0006806432,0.0002038326,0.000105655,0.0006478091,0.0008909958,0.00001406806],"category_scores_gemma":[0.002342958,0.000417274,0.0001666156,0.001108072,0.0003909981,0.000217743,0.0001599271,0.001589959,0.00001291387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008813784,"about_ca_system_score_gemma":0.000387323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002943006,"about_ca_topic_score_gemma":0.00004453343,"domain_scores_codex":[0.9957545,0.0003582005,0.001252096,0.001351454,0.0003782355,0.0009054569],"domain_scores_gemma":[0.9962027,0.00174988,0.000775263,0.0007030891,0.0004186241,0.0001504305],"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":[0.0009256352,0.0006252505,0.002808486,0.0003113883,0.0002375156,0.00002095024,0.01621603,0.797798,0.000533519,0.006694466,0.1266355,0.04719322],"study_design_scores_gemma":[0.001835445,0.000406079,0.00002845213,0.00168244,0.00004077768,0.000004568373,0.001398558,0.9343575,0.00003278471,0.03497812,0.02464282,0.0005924545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003025325,0.01181678,0.9636213,0.001975267,0.01720015,0.001731604,0.0001588507,0.0001094978,0.0003612895],"genre_scores_gemma":[0.2861974,0.0283389,0.5460991,0.01477872,0.03405263,0.003052756,0.01177866,0.001041426,0.07466036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4175221,"threshold_uncertainty_score":0.9998279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3202862609622233,"score_gpt":0.4602469041981359,"score_spread":0.1399606432359125,"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."}}