{"id":"W1991773227","doi":"10.1002/aic.11104","title":"Dynamic optimization of electric arc furnace operation","year":2007,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Electric arc furnace; Process (computing); Mathematical optimization; Process optimization; Optimization problem; Work (physics); Mathematical model; Chemical process; Computer science; Work in process; Engineering; Process engineering; Mechanical engineering; Mathematics; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005967612,0.0005421576,0.000607883,0.0005276519,0.0003366725,0.0009612097,0.0005069323,0.0006497533,0.002953632],"category_scores_gemma":[0.001387525,0.0004937977,0.0003393526,0.0003491444,0.000548536,0.000594489,0.0004675504,0.0004576388,0.0002841682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040156,"about_ca_system_score_gemma":0.001041003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005681832,"about_ca_topic_score_gemma":0.003119906,"domain_scores_codex":[0.9998426,0.00004792701,0.000005528127,0.00002499129,0.0000452442,0.00003364912],"domain_scores_gemma":[0.9997597,0.0001398464,0.00003290703,0.00001277982,0.00004114274,0.00001365904],"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":[0.00001508345,0.000008085208,0.00007208693,0.000009804697,0.000002952169,0.00001161252,0.000004660542,0.9957187,0.0006291646,0.001591875,0.00005540909,0.001880636],"study_design_scores_gemma":[0.000006142296,0.0000123735,0.00005571663,0.000001895903,0.000001756344,0.000002909487,0.000004920777,0.9986468,0.0003861355,0.000699585,0.0001800043,0.00000175819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2770566,0.000484232,0.6760692,0.000536659,0.00004476815,0.0001669597,0.0002900023,0.0005224458,0.04482922],"genre_scores_gemma":[0.9794971,0.00009849176,0.01654211,0.00001751248,0.000004897227,0.00006991615,0.00007180668,0.0000323116,0.003665848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005681832,"threshold_uncertainty_score":0.01129752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003127525684716285,"score_gpt":0.212162771091789,"score_spread":0.2090352454070727,"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."}}