{"id":"W2921006553","doi":"10.1017/s1431927618016215","title":"Secondary Fluorescence Correction for Characteristic and Bremsstrahlung X-Rays Using Monte Carlo X-ray Depth Distributions Applied to Bulk and Multilayer Materials","year":2019,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"X-ray Spectroscopy and Fluorescence Analysis","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; McGill University","funders":"","keywords":"Monte Carlo method; Bremsstrahlung; X-ray fluorescence; Computation; Physics; Dynamic Monte Carlo method; Statistical physics; Computational physics; Algorithm; Fluorescence; Optics; Computer science; Photon; Mathematics; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000237194,0.0003649169,0.0006797966,0.0002038425,0.0004724443,0.0003418413,0.000125696,0.00009595915,0.0001819879],"category_scores_gemma":[0.000008273897,0.0003572932,0.0001286692,0.0003160105,0.0001161232,0.0002156541,0.0001052821,0.0001631171,0.00001145213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000476077,"about_ca_system_score_gemma":0.00004777499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001026709,"about_ca_topic_score_gemma":0.00005547076,"domain_scores_codex":[0.9981669,0.00004083443,0.0004454219,0.0007664909,0.0001032732,0.0004771005],"domain_scores_gemma":[0.9991307,0.00007343535,0.000191653,0.000309759,0.0000956196,0.0001988053],"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":[0.0001277753,0.00005800748,0.02311836,0.00005865045,0.0003306395,6.231271e-7,0.000388023,0.00006569637,0.9721188,0.00009488715,0.00009198043,0.003546537],"study_design_scores_gemma":[0.001731804,0.0001638295,0.062806,0.0001659833,0.002250708,0.000009015423,0.001561716,0.00903815,0.9198426,0.0001099091,0.001210022,0.001110285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748569,0.0001985183,0.02315765,0.00005466556,0.0001918417,0.0006146316,0.0008552141,0.00002532445,0.00004531721],"genre_scores_gemma":[0.9933206,0.00004336821,0.005779729,0.00005969481,0.0001465373,0.000057843,0.0002281013,0.0000308965,0.0003331673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05227624,"threshold_uncertainty_score":0.9998879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007573319637424513,"score_gpt":0.2528852914321075,"score_spread":0.2453119717946829,"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."}}