{"id":"W2117403388","doi":"10.1017/s1431927606067961","title":"Determination of the Backscattered Yield Coefficient by Monte Carlo Calculations and Comparison with Experimental Measurements","year":2006,"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":"McGill University","funders":"","keywords":"Monte Carlo method; Microanalysis; Yield (engineering); Materials science; Statistical physics; Computational physics; Analytical Chemistry (journal); Statistics; Physics; Mathematics; Chemistry; Chromatography; Composite material","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":[0.001247119,0.0007380852,0.000587122,0.0008103116,0.0005767596,0.0005703453,0.001002605,0.0004697821,0.003895377],"category_scores_gemma":[0.003308456,0.0004981442,0.0004854943,0.0009711652,0.0003551776,0.000762242,0.0003894878,0.0003794583,0.0009011891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008244598,"about_ca_system_score_gemma":0.000460203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002316684,"about_ca_topic_score_gemma":0.002340745,"domain_scores_codex":[0.9994055,0.0001289012,0.00003571274,0.0000675931,0.0003177997,0.00004450451],"domain_scores_gemma":[0.9989392,0.0004517229,0.000104953,0.0001578428,0.0003296415,0.00001669359],"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":[0.0009776162,0.0004043848,0.01365827,0.0006899431,0.0002341607,0.0005523831,0.0004957512,0.5112319,0.3810511,0.02614723,0.003481899,0.06107536],"study_design_scores_gemma":[0.00003918214,0.0001051169,0.003417351,0.00002961004,0.00006156983,0.0003176614,0.00006137467,0.7509049,0.2413883,0.001957054,0.001669535,0.0000484857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5225686,0.0009937847,0.4569914,0.0001854689,0.000102,0.0001390356,0.001184626,0.002842091,0.01499303],"genre_scores_gemma":[0.9416391,0.0003786039,0.0555642,0.0000398398,0.000007888209,0.0000895401,0.000520746,0.0003536375,0.001406441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003895377,"threshold_uncertainty_score":0.0130313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480621942714895,"score_gpt":0.2803476476319739,"score_spread":0.265541428204825,"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."}}