{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006384461,0.00009325792,0.0001503687,0.0001904745,0.00005616306,0.00002483461,0.00009092103,0.00007556217,0.00003071964],"category_scores_gemma":[0.00005682784,0.00009230488,0.00004816092,0.0002635956,0.000007705205,0.0003433106,0.000005375883,0.0002136268,0.000005827773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001554429,"about_ca_system_score_gemma":0.00002245844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000013888,"about_ca_topic_score_gemma":0.000005893154,"domain_scores_codex":[0.9990516,0.00003228352,0.0004938267,0.00006817663,0.0001683497,0.0001857894],"domain_scores_gemma":[0.9995056,0.00003851097,0.0001416397,0.0001001305,0.0001469832,0.00006718352],"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":[0.00001149747,0.000008933625,0.0002617928,0.000008946636,0.00002891478,0.00000312583,0.00006249618,0.9680306,0.02340187,0.00004165026,0.00006780701,0.008072329],"study_design_scores_gemma":[0.0005049081,0.00004580161,0.001187009,0.00002156287,0.00001820309,0.0001084402,0.00003856799,0.9949399,0.002831883,0.00005492294,0.0001513325,0.00009743996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0260292,0.00091054,0.9710145,0.00002518132,0.0004341954,0.0001055675,5.047229e-7,0.0000662082,0.001414097],"genre_scores_gemma":[0.945672,0.000466168,0.05360666,0.00001513966,0.0001299792,0.000001239666,0.000004087081,0.00002842035,0.0000762649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9196429,"threshold_uncertainty_score":0.3764084,"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."}}