{"id":"W1708051103","doi":"10.1017/s1431927602101206","title":"Understanding Complex Microstructures With High-Resolution Microanalysis in the Transmission Electron Microscope.","year":2002,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Fibics (Canada); Natural Resources Canada; McMaster University","funders":"","keywords":"Microanalysis; Transmission electron microscopy; Conventional transmission electron microscope; Materials science; Electron microscope; Microstructure; High-resolution transmission electron microscopy; Scanning transmission electron microscopy; Microscope; High resolution; Resolution (logic); Electron tomography; Analytical Chemistry (journal); Optics; Nanotechnology; Metallurgy; Chemistry; Physics; Geology; Computer science; Chromatography; Artificial intelligence; Remote sensing","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":[0.0005095981,0.0003408785,0.0003374902,0.0007894427,0.0003771885,0.0005907022,0.0005873924,0.0007587951,0.002837102],"category_scores_gemma":[0.0007690588,0.0004687273,0.00023032,0.0003925005,0.0005562057,0.001646703,0.0004816603,0.000918289,0.0008193303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002086711,"about_ca_system_score_gemma":0.0001832669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006575502,"about_ca_topic_score_gemma":0.002007161,"domain_scores_codex":[0.999859,0.00002492917,0.000008328124,0.00001630645,0.00007926705,0.00001210447],"domain_scores_gemma":[0.9996954,0.0001537,0.00002569369,0.00003868293,0.00006426289,0.00002220087],"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":[0.00004366738,0.00003781538,0.0005499016,0.0005651049,0.00003234268,0.0004150208,0.00009259827,0.0008173191,0.9459425,0.006308562,0.003575278,0.04161992],"study_design_scores_gemma":[0.00004434896,0.0001784245,0.013092,0.0002272034,0.0001026806,0.007292006,0.0004668334,0.04494286,0.8238707,0.02441657,0.08528876,0.00007765194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09528533,0.07246557,0.8102831,0.002130876,0.0006091004,0.0001971476,0.0007007304,0.002264516,0.01606364],"genre_scores_gemma":[0.3285323,0.05316087,0.6067425,0.0005175945,0.0001889158,0.0001740766,0.0005785997,0.0002383148,0.009866686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002837102,"threshold_uncertainty_score":0.009491026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0274592005018696,"score_gpt":0.2685402120093913,"score_spread":0.2410810115075217,"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."}}