{"id":"W4417486371","doi":"10.1016/j.ifacol.2025.12.392","title":"Self-optimizing control of secondary grinding – coping without particle size monitoring","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Particle size; Grinding; Limiting; Sample size determination; Particle-size distribution; Control variable; Product (mathematics); Statistical process control","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002434077,0.0002492036,0.0004116299,0.0001002222,0.0001558568,0.00006075808,0.0001911328,0.0001153362,0.00003783984],"category_scores_gemma":[0.00009230762,0.0002534513,0.00009850977,0.0003475107,0.00003563092,0.0002325957,0.00004047432,0.0003458135,0.000008803699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001039957,"about_ca_system_score_gemma":0.00004945746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001435109,"about_ca_topic_score_gemma":0.000004708389,"domain_scores_codex":[0.9986622,0.00002798726,0.0004331722,0.0002530317,0.0001783735,0.0004452723],"domain_scores_gemma":[0.9993355,0.0002217886,0.00007604698,0.0002171281,0.00005912306,0.00009043148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008962654,0.0001386841,0.1122643,0.002892649,0.0006963984,0.00004215709,0.002870856,0.1081188,0.7523677,0.0003150246,0.00002595273,0.02017782],"study_design_scores_gemma":[0.00742476,0.000138634,0.01108837,0.002778285,0.0004335395,0.00003931351,0.002354797,0.699325,0.2732779,0.0001400033,0.001605577,0.001393768],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832135,0.002281809,0.008110997,0.0002060975,0.0007787303,0.0001868903,0.00001538364,0.0006706305,0.004535924],"genre_scores_gemma":[0.8941479,0.00007570777,0.1050678,0.00005967895,0.0002522462,0.00001515137,0.000002778941,0.00003769774,0.0003410576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5912063,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007402315560563492,"score_gpt":0.2385088648231201,"score_spread":0.2311065492625566,"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."}}