{"id":"W2832034232","doi":"10.1088/1361-6560/aad312","title":"Optimized <i>I</i>-values for use with the Bragg additivity rule and their impact on proton stopping power and range uncertainty","year":2018,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Radiation Therapy and Dosimetry","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Engineering and Physical Sciences Research Council; Cancer Research UK","keywords":"Stopping power; Range (aeronautics); Proton; Monte Carlo method; Proton therapy; Bragg peak; Beam (structure); Bar (unit); Computational physics; Additive function; Error bar; Physics; Materials science; Mathematics; Statistics; Nuclear physics; Optics; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001819013,0.0009182657,0.0005152758,0.0008468295,0.0003541933,0.0011556,0.0009365638,0.0007251052,0.00119183],"category_scores_gemma":[0.00365762,0.0004117839,0.0005906834,0.000670278,0.0006860263,0.001246174,0.0009827338,0.0008159793,0.0004517294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008141599,"about_ca_system_score_gemma":0.0007723737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001092251,"about_ca_topic_score_gemma":0.001749076,"domain_scores_codex":[0.9988477,0.0001747867,0.00007881545,0.0002343745,0.000572234,0.00009196481],"domain_scores_gemma":[0.9987742,0.0005427607,0.0002348508,0.0002155943,0.0002061086,0.00002648649],"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":[0.0009136588,0.000244034,0.01392532,0.001100015,0.0002006787,0.0003634055,0.0003175349,0.4645508,0.365757,0.01812717,0.002806476,0.1316939],"study_design_scores_gemma":[0.00005459044,0.0007218952,0.007117631,0.0001771227,0.0002152552,0.0004259062,0.0001670142,0.2908853,0.6727008,0.01215547,0.01519211,0.000186932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4655051,0.005878139,0.491599,0.0008349299,0.0001620565,0.0001914334,0.001387193,0.001601158,0.03284107],"genre_scores_gemma":[0.8417684,0.001136773,0.1535449,0.0002423411,0.0000168649,0.0001747966,0.0006202955,0.0004597293,0.002035875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001819013,"threshold_uncertainty_score":0.009620011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010094019403621,"score_gpt":0.3813363040256429,"score_spread":0.2803269020852809,"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."}}