{"id":"W3038186642","doi":"10.1017/s143192760003645x","title":"From the Scanning Electron Microscope to the Scanning Electron Macroscope with X-Ray Microanalysis in the ESEM","year":2000,"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":"Université de Sherbrooke","funders":"","keywords":"Environmental scanning electron microscope; Microanalysis; Scanning electron microscope; Intensity (physics); Electron; Electron microscope; Monte Carlo method; Scattering; Atomic physics; Analytical Chemistry (journal); Optics; Microscope; Materials science; Chemistry; Physics; Nuclear physics; Mathematics; Statistics; Chromatography","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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00249695,0.0008729094,0.001028514,0.000318783,0.001773485,0.001585536,0.002437406,0.0002200429,0.0009210508],"category_scores_gemma":[0.00005117952,0.0004613129,0.0003328083,0.002942612,0.0005686595,0.0003836816,0.0001992515,0.001064783,0.0002248846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003133094,"about_ca_system_score_gemma":0.0002302088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009940885,"about_ca_topic_score_gemma":0.009243393,"domain_scores_codex":[0.993985,0.0009252791,0.0009460489,0.001572173,0.0006824535,0.001889023],"domain_scores_gemma":[0.9971725,0.0004181928,0.0003251407,0.001755284,0.0001370965,0.0001917273],"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":[0.0004430725,0.0001215616,0.002401665,0.00001035344,0.0002086674,0.00001692416,0.005024631,0.0002176086,0.9823911,0.00003855475,0.007576806,0.001549015],"study_design_scores_gemma":[0.0005899771,0.0004226701,0.002713671,0.0001461607,0.0007768337,0.00004077172,0.001307072,0.00007790202,0.9461148,0.0001462021,0.04696078,0.0007032093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979796,0.008616526,0.001410049,0.008248578,0.00006296963,0.0010236,0.00009319573,0.0001589404,0.0005901431],"genre_scores_gemma":[0.9807069,0.00116956,0.007569061,0.007619191,0.0003535929,0.000299005,0.00009812397,0.0001125707,0.002071977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03938397,"threshold_uncertainty_score":0.9999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006620956244810449,"score_gpt":0.2689001056509835,"score_spread":0.262279149406173,"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."}}