{"id":"W2095232344","doi":"10.1002/cjce.20487","title":"SAG mill system diagnosis using multivariate process variable analysis","year":2011,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mill; Process (computing); Process engineering; Principal component analysis; Grinding; Reliability engineering; Multivariate statistics; Computer science; Variable (mathematics); Engineering; Mathematics; Artificial intelligence; Machine learning; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0004223784,0.0004825676,0.0003951559,0.001214416,0.0002031401,0.000617286,0.0002562382,0.0003318185,0.0009946745],"category_scores_gemma":[0.001049516,0.0001392284,0.0003050094,0.0005736016,0.0001412684,0.0002932719,0.0002765046,0.0004117633,0.0001922702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003006708,"about_ca_system_score_gemma":0.0003374947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002902944,"about_ca_topic_score_gemma":0.002296831,"domain_scores_codex":[0.9996541,0.00006740544,0.00001990393,0.00007013469,0.0001503013,0.00003820987],"domain_scores_gemma":[0.9995362,0.0002015163,0.0000783495,0.00002861938,0.0001372557,0.00001804492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009580425,0.0005225684,0.04689693,0.0002687599,0.0002134234,0.000410644,0.000203427,0.1849729,0.1740872,0.001454886,0.001936918,0.5880743],"study_design_scores_gemma":[0.00001277999,0.00009885853,0.01398242,0.000004254336,0.00001805461,0.00005804387,0.00002963423,0.9719028,0.01298983,0.0005236263,0.0003656149,0.00001407267],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4608452,0.0001997479,0.5342064,0.0001260846,0.00003436481,0.0001005173,0.0003464737,0.00201425,0.002126987],"genre_scores_gemma":[0.9671915,0.00004340862,0.03215317,0.00000957502,0.000007983666,0.000018679,0.0001427174,0.0000163634,0.0004167549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002902944,"threshold_uncertainty_score":0.005772054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028207120718432,"score_gpt":0.1984917543773456,"score_spread":0.1782096831701613,"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."}}