{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008196259,0.000913301,0.0008674341,0.0006826818,0.001000207,0.003884558,0.002218469,0.001919085,0.002144626],"category_scores_gemma":[0.009817101,0.0008471084,0.001490423,0.001352388,0.001409166,0.003247735,0.002092083,0.002757578,0.0007962837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002168634,"about_ca_system_score_gemma":0.002401414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009486636,"about_ca_topic_score_gemma":0.004876232,"domain_scores_codex":[0.99652,0.00177379,0.0002434804,0.0002715803,0.001017195,0.0001738334],"domain_scores_gemma":[0.9945635,0.002924323,0.0001133467,0.0009677907,0.001247767,0.0001833108],"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":[0.0003941265,0.0004808647,0.004458658,0.0009612474,0.000145391,0.001260882,0.00620231,0.7131117,0.02088856,0.04168371,0.004852741,0.2055599],"study_design_scores_gemma":[0.0001216202,0.0003894211,0.00122792,0.0002139483,0.00007615726,0.0004617278,0.001164593,0.8484882,0.03168133,0.02577883,0.09020903,0.0001873398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09767035,0.000788085,0.8829342,0.002814842,0.0001732965,0.0003149074,0.0003698888,0.001746075,0.01318833],"genre_scores_gemma":[0.4932115,0.002036628,0.497776,0.00034636,0.00006491175,0.0003356712,0.0008815808,0.0004114205,0.004935884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009486636,"threshold_uncertainty_score":0.04334652,"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."}}