{"id":"W4388871702","doi":"10.1016/j.apradiso.2023.111109","title":"A benchmark for Monte Carlo simulations in gamma-ray spectrometry Part II: True coincidence summing correction factors","year":2023,"lang":"en","type":"article","venue":"Applied Radiation and Isotopes","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Coincidence; Benchmark (surveying); Monte Carlo method; Gamma ray spectrometry; Detector; Computer science; Physics; Computational physics; Algorithm; Mathematics; Optics; Statistics; Chemistry; Radiochemistry","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":[0.005228509,0.001180838,0.00101307,0.001132254,0.001249561,0.002416426,0.002951278,0.002302923,0.006147772],"category_scores_gemma":[0.03574412,0.0006841234,0.0007742986,0.002303056,0.0007184788,0.001930627,0.001134533,0.001451668,0.001506605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002148104,"about_ca_system_score_gemma":0.002988126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008313341,"about_ca_topic_score_gemma":0.006618712,"domain_scores_codex":[0.9968883,0.001389472,0.0001756835,0.0003210301,0.001003708,0.0002218451],"domain_scores_gemma":[0.9755555,0.01583225,0.0007885448,0.003172264,0.004166585,0.000484746],"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":[0.000614438,0.0001897555,0.002750917,0.0002471491,0.0001046352,0.0001046538,0.0001077991,0.9272162,0.002483229,0.0268436,0.005543975,0.03379368],"study_design_scores_gemma":[0.00007362442,0.00008431547,0.000339042,0.00004277455,0.00002475837,0.00006285377,0.00003707941,0.9804462,0.00578698,0.01011734,0.002959818,0.0000252621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.237571,0.003697796,0.6867106,0.001734155,0.0005509644,0.0003675754,0.004575373,0.01460692,0.05018561],"genre_scores_gemma":[0.6623452,0.0006760566,0.3237669,0.0004696083,0.0001061725,0.0003719668,0.003424311,0.004676454,0.004163323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008313341,"threshold_uncertainty_score":0.02765131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203437188767865,"score_gpt":0.2501013945486422,"score_spread":0.2380670226609636,"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."}}