{"id":"W2972974626","doi":"10.3390/pr7090610","title":"Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control","year":2019,"lang":"en","type":"article","venue":"Processes","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Computer science; Replicate; Raw material; Process (computing); Control variable; Process engineering; Engineering; Mathematics; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002467459,0.0001383977,0.0002597036,0.00003388001,0.00002718562,0.00005736696,0.0001628898,0.0000746189,0.0000164323],"category_scores_gemma":[0.0002598799,0.000139456,0.000007468645,0.0001054301,0.00004126838,0.0008973705,0.00005140971,0.00008943064,0.000003184193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005237066,"about_ca_system_score_gemma":0.00005007645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008660814,"about_ca_topic_score_gemma":0.000009173665,"domain_scores_codex":[0.999118,0.00003089487,0.0002511881,0.0002968045,0.0001068094,0.0001962511],"domain_scores_gemma":[0.9994625,0.0001509778,0.00004942764,0.0002375822,0.0000703935,0.00002913549],"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":[0.00004574512,0.000009640558,0.003180242,0.0008016569,0.00002278076,9.046919e-7,0.0003215268,0.9929504,0.002235436,0.00008214632,0.000002788203,0.0003467494],"study_design_scores_gemma":[0.001150482,0.00001192003,0.00006823419,0.0001252353,0.0000148955,0.000003621632,0.00005992246,0.9963503,0.0009128022,0.001146571,0.00001773819,0.0001382667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08105551,0.0002196479,0.9163354,0.00001280301,0.0001652796,0.0008428812,0.00015921,0.0002008171,0.001008478],"genre_scores_gemma":[0.9932334,0.00006879057,0.006481493,0.00001406143,0.000053664,0.00007984479,0.0000394424,0.00002489761,0.000004391078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9121779,"threshold_uncertainty_score":0.5686851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165765066528363,"score_gpt":0.2320001517896536,"score_spread":0.22034250112437,"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."}}