{"id":"W2062203432","doi":"10.1118/1.4889537","title":"TH‐A‐19A‐04: Latent Uncertainties and Performance of a GPU‐Implemented Pre‐Calculated Track Monte Carlo Method","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; McGill University","funders":"","keywords":"Monte Carlo method; Benchmark (surveying); Graphics processing unit; Computer science; Track (disk drive); Computational physics; Proton; Physics; Bragg peak; Algorithm; Mathematics; Statistics; Nuclear physics; Parallel computing","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":[],"consensus_categories":[],"category_scores_codex":[0.0002665317,0.0001409078,0.0002707995,0.00004005035,0.00008291334,0.00001946668,0.000146297,0.00007070861,0.0001870964],"category_scores_gemma":[0.00003890553,0.0001132506,0.00007844834,0.000194134,0.0001389129,0.00008981691,0.00006269196,0.0002086161,0.000004807941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001186905,"about_ca_system_score_gemma":0.00002723709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001384546,"about_ca_topic_score_gemma":0.000002952368,"domain_scores_codex":[0.9989123,0.00005289049,0.0002924337,0.0002047561,0.000337308,0.0002003207],"domain_scores_gemma":[0.999394,0.00008665028,0.0001357686,0.0002116377,0.00007194681,0.00009999634],"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":[0.00006260959,0.0001704519,0.1070502,0.0001017357,0.0002141838,4.961787e-7,0.001255214,0.004359782,0.0004736156,0.004966796,0.001003787,0.8803411],"study_design_scores_gemma":[0.002007624,0.0004820074,0.04916393,0.0001159305,0.0001055622,0.000001631623,0.0004198835,0.8926631,0.04240176,0.0027553,0.009429554,0.0004537719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834745,0.00002722413,0.01544562,0.0003369258,0.0001033958,0.0001296607,0.000009965301,0.00009009134,0.000382639],"genre_scores_gemma":[0.999266,0.00001298842,0.0003476406,0.0000748125,0.0001167476,0.00001460622,0.000003460739,0.000011487,0.0001522779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8883033,"threshold_uncertainty_score":0.4618226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305501229455447,"score_gpt":0.2720685533569824,"score_spread":0.2590135410624279,"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."}}