{"id":"W3023537583","doi":"10.1016/j.ejmp.2021.10.003","title":"On the use of machine learning methods for mPSD calibration in HDR brachytherapy","year":2021,"lang":"en","type":"preprint","venue":"Physica Medica","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Calibration; Brachytherapy; Linear regression; Random forest; Artificial neural network; Detector; Scintillator; Dosimetry; Mathematics; Statistics; Algorithm; Computer science; Optics; Physics; Artificial intelligence; Nuclear medicine","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":[0.004021863,0.0007644614,0.0008212751,0.0009465438,0.0006681131,0.001607622,0.001245385,0.001324951,0.002393431],"category_scores_gemma":[0.01154658,0.000504093,0.0008489588,0.0008704482,0.0008740433,0.001235909,0.001471191,0.001689575,0.0007710845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007796951,"about_ca_system_score_gemma":0.0006896585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00337624,"about_ca_topic_score_gemma":0.002358001,"domain_scores_codex":[0.9982511,0.0009857275,0.00008357022,0.0002021783,0.0004180479,0.00005939442],"domain_scores_gemma":[0.9948251,0.00382473,0.0002230095,0.0004231985,0.0006486108,0.00005539168],"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":[0.0001774632,0.00009511726,0.001738332,0.00025404,0.000164143,0.0000671965,0.0001277965,0.4719584,0.006526313,0.02155264,0.002392912,0.4949456],"study_design_scores_gemma":[0.000004147042,0.00001880668,0.0006606793,0.00003164563,0.000009882422,0.00003487272,0.00001094036,0.988638,0.002745022,0.00639644,0.0014378,0.00001178894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01443144,0.002569224,0.9785104,0.0005519537,0.0001147646,0.00004916693,0.00006198719,0.0006268019,0.003084316],"genre_scores_gemma":[0.5227539,0.002471557,0.4659938,0.0003208083,0.0002630259,0.0001215627,0.0002625104,0.0004829312,0.007330004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004021863,"threshold_uncertainty_score":0.02126992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04704118007009968,"score_gpt":0.3745648581108261,"score_spread":0.3275236780407264,"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."}}