{"id":"W2010093149","doi":"10.1002/cjce.20090","title":"Experiences in applying data‐driven modelling technology to steelmaking processes","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"ArcelorMittal (Canada)","funders":"McMaster University","keywords":"Steelmaking; Multivariate statistics; Computer science; Principal (computer security); Software; Partial least squares regression; Missing data; Data mining; Industrial engineering; Engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001419206,0.0001099838,0.0001949432,0.0003444442,0.00005471711,0.00003042593,0.0005583499,0.00007417422,0.00000514574],"category_scores_gemma":[0.0001300695,0.00009248359,0.00002245291,0.0005852256,0.00003038218,0.0001472955,0.00001804559,0.0003367595,0.000002956081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001537873,"about_ca_system_score_gemma":0.0001602722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003033243,"about_ca_topic_score_gemma":0.0004770504,"domain_scores_codex":[0.9991793,0.000004703581,0.000309186,0.00009153234,0.0001292354,0.0002860587],"domain_scores_gemma":[0.9994938,0.00004107125,0.0000339213,0.0001846831,0.00004859851,0.000197942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002182411,0.000001188672,0.00008150251,0.00002923507,0.00001377573,0.00005103647,0.002146333,0.9879899,0.009206617,0.00003126941,0.00003702741,0.000409922],"study_design_scores_gemma":[0.0001891556,0.00001163822,0.000004817415,0.0002064906,0.000005545035,0.0005139555,0.0006716744,0.975744,0.01675403,0.00001032678,0.005716975,0.0001713742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833559,0.001189714,0.0146958,0.0001452029,0.0003102965,0.0001209479,0.000002076797,0.00005763258,0.0001223886],"genre_scores_gemma":[0.9989489,0.000005677588,0.0008318454,0.00001832151,0.000144307,0.00002411592,4.165761e-7,0.00002099109,0.000005442043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01559294,"threshold_uncertainty_score":0.3771372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038404026196152,"score_gpt":0.2142948072780493,"score_spread":0.1939107670160878,"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."}}