{"id":"W4402878835","doi":"10.1016/j.radphyschem.2024.112248","title":"Enhancement of beta spectrometry using double scintillators and ML-based unfolding","year":2024,"lang":"en","type":"article","venue":"Radiation Physics and Chemistry","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Mitacs; CANDU Owners Group","keywords":"Scintillator; BETA (programming language); Mass spectrometry; Radiochemistry; Chemistry; Gamma ray spectrometry; Analytical Chemistry (journal); Nuclear physics; Physics; Chromatography; Computer science; Optics; Detector","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":[0.0009037235,0.0009847375,0.0004143339,0.0008340001,0.0001907408,0.0005703948,0.0007223971,0.0004823012,0.0009687791],"category_scores_gemma":[0.001212929,0.0002897622,0.0005907653,0.0005316472,0.0002828381,0.0008928448,0.0009020341,0.0004775403,0.0005287215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004892197,"about_ca_system_score_gemma":0.0005025194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261745,"about_ca_topic_score_gemma":0.001731722,"domain_scores_codex":[0.9997291,0.00004089585,0.00001378125,0.00008226926,0.00009475973,0.00003919844],"domain_scores_gemma":[0.9996144,0.000104339,0.00006203527,0.00005712706,0.0001372088,0.00002486026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006049616,0.0001173873,0.006995352,0.0003326084,0.0001201629,0.0002932146,0.0001519733,0.067728,0.6657783,0.002151276,0.000976227,0.2547506],"study_design_scores_gemma":[0.00001942122,0.0002041644,0.004237861,0.00001965906,0.00006005027,0.0002327867,0.00004154566,0.5844625,0.40495,0.001044141,0.004688895,0.00003898657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2503473,0.000899922,0.740391,0.0001816696,0.0001093156,0.0000754464,0.0002640152,0.004453873,0.003277559],"genre_scores_gemma":[0.6240182,0.0004508249,0.3710822,0.0001475089,0.00005481046,0.00007791854,0.0007590107,0.0004378043,0.002971789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001261745,"threshold_uncertainty_score":0.004779398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108584224237043,"score_gpt":0.2510973678277251,"score_spread":0.2402389454040208,"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."}}