{"id":"W2805353369","doi":"10.1017/s1431927618000417","title":"Use of an Annular Silicon Drift Detector (SDD) Versus a Conventional SDD Makes Phase Mapping a Practical Solution for Rare Earth Mineral Characterization","year":2018,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"FLSmidth","keywords":"Silicon drift detector; Characterization (materials science); Detector; Silicon; Materials science; Phase (matter); Beneficiation; Image resolution; Resolution (logic); Grinding; Scanning electron microscope; Optics; Analytical Chemistry (journal); Optoelectronics; Nanotechnology; Computer science; Physics; Chemistry; Metallurgy; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002468254,0.0001667805,0.0003185525,0.0001516037,0.0002425647,0.000104908,0.00007707934,0.0001056884,0.0003531049],"category_scores_gemma":[0.0001899149,0.0001448843,0.0001506074,0.000420504,0.0001892473,0.0004540283,0.00001189601,0.00009315572,0.00001425773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003701777,"about_ca_system_score_gemma":0.0000435809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004066536,"about_ca_topic_score_gemma":0.0004253727,"domain_scores_codex":[0.9986765,0.0001765738,0.0003170609,0.0003834488,0.000136952,0.0003094719],"domain_scores_gemma":[0.9990949,0.0002497014,0.0001758934,0.000150096,0.0001844655,0.0001449499],"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":[0.001330823,0.0001440578,0.002178506,0.00003497782,0.0001254464,0.000002144825,0.0001138583,0.000004086178,0.9018093,0.0000151018,0.00004298992,0.09419865],"study_design_scores_gemma":[0.005094898,0.004623519,0.1829348,0.00006010921,0.000841113,0.00002424659,0.0001262057,0.1942844,0.5862594,0.0002416706,0.02479345,0.000716242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544835,0.00008756088,0.04442242,0.0001678862,0.0001464586,0.0002149164,0.0004497937,0.00001934651,0.000008099596],"genre_scores_gemma":[0.9500372,0.00002005926,0.04806628,0.0001327448,0.0002464746,0.000004926137,0.001248289,0.000005825401,0.0002382344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.31555,"threshold_uncertainty_score":0.5908208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04274151065962736,"score_gpt":0.3098644095510461,"score_spread":0.2671228988914188,"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."}}