{"id":"W2998344628","doi":"10.1155/2020/2134516","title":"Multivariate Statistical Analysis on a SEM/EDS Phase Map of Rare Earth Minerals","year":2020,"lang":"en","type":"article","venue":"Scanning","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Hydro-Québec; McGill University","keywords":"Principal component analysis; Non-negative matrix factorization; Independent component analysis; Preprocessor; Multivariate statistics; Pattern recognition (psychology); Phase (matter); Computer science; Artificial intelligence; Chemometrics; Identification (biology); Analytical Chemistry (journal); Mathematics; Chemistry; Statistics; Matrix decomposition; Chromatography; Physics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001124584,0.0006206973,0.0003500543,0.001760062,0.0003190113,0.0005013374,0.0003013714,0.0002360102,0.002119953],"category_scores_gemma":[0.002221355,0.0001628008,0.0005497,0.001363473,0.0004221582,0.0006070675,0.0004228405,0.0004572023,0.0003615794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002618795,"about_ca_system_score_gemma":0.0005438111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001802499,"about_ca_topic_score_gemma":0.002445772,"domain_scores_codex":[0.9996163,0.00006805811,0.00002437339,0.0001022177,0.0001563337,0.00003269053],"domain_scores_gemma":[0.9992811,0.000209645,0.00008764762,0.0000827828,0.0003219378,0.00001684234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004745411,0.0001788683,0.01299001,0.000628852,0.0001687027,0.0003353458,0.0004145836,0.06506991,0.3942434,0.01162755,0.004130888,0.5097373],"study_design_scores_gemma":[0.00002198086,0.0002188006,0.06017149,0.00003385957,0.00009308094,0.0005395084,0.0004350733,0.7331785,0.1828577,0.01281815,0.009505698,0.0001262255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1453438,0.0002028967,0.8494387,0.0001418845,0.00004212957,0.0001165382,0.0008895216,0.001811392,0.002013096],"genre_scores_gemma":[0.5317504,0.0003009636,0.4644344,0.00004301074,0.00003342723,0.0001579925,0.001175852,0.0003174504,0.001786439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002119953,"threshold_uncertainty_score":0.007091939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03608360391816863,"score_gpt":0.3337290527614472,"score_spread":0.2976454488432786,"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."}}