{"id":"W3029261949","doi":"10.1017/s1431927620001531","title":"Secondary Fluorescence of 3D Heterogeneous Materials Using a Hybrid Model","year":2020,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; McGill University","funders":"","keywords":"Monte Carlo method; X-ray fluorescence; Voxel; Statistical physics; Dynamic Monte Carlo method; Hybrid Monte Carlo; Secondary electrons; Diffusion; Computational physics; Characterization (materials science); Materials science; Electron; Physics; Computer science; Fluorescence; Optics; Mathematics; Thermodynamics; Nuclear physics; Statistics; Artificial intelligence; Markov chain Monte Carlo","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":[0.0002340904,0.0002617734,0.0006126603,0.0001060947,0.000147688,0.0001117726,0.0003112837,0.00007650434,0.0005949691],"category_scores_gemma":[0.00001928251,0.0002478549,0.0001087715,0.000210857,0.0002131924,0.0001699588,0.0001617644,0.0001161827,0.00001482409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003897493,"about_ca_system_score_gemma":0.0001044503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001591066,"about_ca_topic_score_gemma":0.000008272675,"domain_scores_codex":[0.9982999,0.00008391502,0.0005095178,0.0005484686,0.0001556413,0.0004025823],"domain_scores_gemma":[0.9992356,0.0000174847,0.0002375863,0.0002944256,0.00007586726,0.0001390954],"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":[0.0001000423,0.00003437796,0.00005274811,0.00009323002,0.00004139547,0.000009404117,0.0002577855,0.0001030074,0.9988192,0.00003049346,0.0002189449,0.0002393149],"study_design_scores_gemma":[0.0002092778,0.000114816,0.000004405239,0.00003010653,0.0001807239,0.00002795188,0.00001936244,0.005768358,0.9931594,0.0001389483,0.0001034344,0.0002431923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762077,0.001168328,0.02187249,0.0001150499,0.00003524093,0.0001605645,0.0002841709,0.0001024293,0.0000540671],"genre_scores_gemma":[0.9229898,0.0002213606,0.07609861,0.0005558613,0.0000403919,0.000006489544,0.00002053199,0.00002876676,0.00003813038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05422611,"threshold_uncertainty_score":0.9999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.015548511488995,"score_gpt":0.2708900344938286,"score_spread":0.2553415230048336,"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."}}