{"id":"W4212838144","doi":"10.1016/j.apradiso.2022.110151","title":"Investigating methods of normalization for X-ray fluorescence measurements of zinc in nail clippings using the TOPAS Monte Carlo code","year":2022,"lang":"en","type":"article","venue":"Applied Radiation and Isotopes","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mount Allison University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Monte Carlo method; Zinc; Fluorescence; Physics; Materials science; Optics; Mathematics; Metallurgy","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":[0.004272561,0.0008666253,0.0006018488,0.001036507,0.0009756042,0.001713566,0.002000836,0.000776781,0.004031792],"category_scores_gemma":[0.00972662,0.0006165495,0.0008125877,0.001395296,0.0005759051,0.001080533,0.0009094194,0.001402796,0.000828762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003221115,"about_ca_system_score_gemma":0.002811742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.017765,"about_ca_topic_score_gemma":0.02546949,"domain_scores_codex":[0.9985183,0.0004833851,0.00006598997,0.0002239779,0.0006124942,0.00009589806],"domain_scores_gemma":[0.9964086,0.001883053,0.0002475558,0.0004339931,0.0009510058,0.0000757763],"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":[0.001387338,0.0004172397,0.02981974,0.0007927996,0.0005876685,0.0002914938,0.001038636,0.4347649,0.100093,0.1327593,0.004863575,0.2931843],"study_design_scores_gemma":[0.00003681507,0.00006350956,0.003831264,0.0000659168,0.0000739826,0.0001486417,0.0001216076,0.9053429,0.0741032,0.009980819,0.006164041,0.00006722151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04684639,0.0002800011,0.9456353,0.0001439535,0.000058259,0.0001089352,0.000291325,0.00360031,0.003035591],"genre_scores_gemma":[0.3130408,0.0003080557,0.6788774,0.0001034226,0.00001344306,0.0002444169,0.0005542283,0.00283049,0.004027784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.017765,"threshold_uncertainty_score":0.03532314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03485971981468053,"score_gpt":0.3173523060009289,"score_spread":0.2824925861862484,"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."}}