{"id":"W3213288697","doi":"10.3390/pr9112004","title":"Optimal Scheduling of the Peirce-Smith Converter in the Copper Smelting Process","year":2021,"lang":"en","type":"article","venue":"Processes","topic":"Process Optimization and Integration","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Horizon 2020; European Commission","keywords":"Scheduling (production processes); Copper; Smelting; Computer science; Mathematical optimization; Industrial engineering; Operations research; Process engineering; Engineering; Mathematics; Materials science; Metallurgy","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":[0.0006979945,0.0005533187,0.0007809291,0.0004296039,0.0005226037,0.0008802799,0.000550592,0.0005957976,0.001915409],"category_scores_gemma":[0.0008828011,0.0003279014,0.0003695237,0.0005344386,0.0005494524,0.0004488347,0.0004001807,0.0004966415,0.000141553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374345,"about_ca_system_score_gemma":0.002183947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086192,"about_ca_topic_score_gemma":0.00773575,"domain_scores_codex":[0.9997373,0.00008148364,0.00001041375,0.00004804899,0.00005302025,0.00006976868],"domain_scores_gemma":[0.9997411,0.000129122,0.00004833219,0.00001060231,0.00003440003,0.00003624705],"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":[0.0001482952,0.00004850133,0.0002908643,0.00004571704,0.000008924125,0.000094447,0.00003602392,0.9830917,0.004424277,0.002379555,0.0001860344,0.009245759],"study_design_scores_gemma":[0.0000231799,0.0001268928,0.0002918418,0.000002466074,0.000006896932,0.00001191838,0.00002898811,0.9950455,0.003024424,0.001122394,0.0003081818,0.00000734099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5551841,0.0003509303,0.4313439,0.0002894572,0.00005027536,0.0002118935,0.0001280183,0.0002975189,0.01214389],"genre_scores_gemma":[0.9691103,0.0001277002,0.02851827,0.00001278427,0.000006553247,0.00005079791,0.00004294585,0.00002029603,0.002110373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01086192,"threshold_uncertainty_score":0.02159745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175863389737702,"score_gpt":0.2350231169989874,"score_spread":0.2232644831016104,"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."}}