{"id":"W2560069663","doi":"10.1017/s1431927615004328","title":"Origins and Contrast of the Electron Signals at Low Accelerating Voltage and with Energy-Filtering in the FE-SEM for High Resolution Imaging","year":2015,"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":"Hitachi (Canada); McGill University","funders":"","keywords":"Contrast (vision); Resolution (logic); Energy (signal processing); Electron; Voltage; Acceleration voltage; Materials science; Physics; Optics; Computer science; Artificial intelligence; Nuclear physics; Cathode ray; Quantum mechanics","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":[0.002064427,0.0005921645,0.000300433,0.002322384,0.0007041382,0.001521708,0.001189873,0.001124837,0.001854092],"category_scores_gemma":[0.003409603,0.0009380679,0.0003064396,0.0007596228,0.001598845,0.001808385,0.0007925788,0.002237043,0.0005090507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007729403,"about_ca_system_score_gemma":0.0005571205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000640842,"about_ca_topic_score_gemma":0.001550856,"domain_scores_codex":[0.9993293,0.000174706,0.00003574551,0.00012134,0.0002925316,0.00004628938],"domain_scores_gemma":[0.9984089,0.0007374139,0.0002077816,0.0001980321,0.0003168645,0.0001311059],"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":[0.0006156795,0.00006176541,0.001278042,0.0002392179,0.00001308883,0.0005519168,0.0002468709,0.0006915251,0.9374247,0.01983405,0.0009016766,0.03814157],"study_design_scores_gemma":[0.00001396482,0.00004249644,0.00262702,0.00002929527,0.00001413712,0.0007111007,0.00003772617,0.005490335,0.9844752,0.002664745,0.003870046,0.00002403649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.286119,0.006119099,0.6835838,0.001386072,0.0002097213,0.0002593801,0.0003607123,0.001569991,0.02039226],"genre_scores_gemma":[0.5784339,0.00310933,0.4053475,0.0001620211,0.00008085242,0.0001151598,0.0002658613,0.001077387,0.01140795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002322384,"threshold_uncertainty_score":0.01091784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096279290604692,"score_gpt":0.2593115527258586,"score_spread":0.2483487598198117,"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."}}