{"id":"W4398217048","doi":"10.1002/cjce.25343","title":"New <scp>PID</scp> parameter tuning based on improved dung beetle optimization algorithm","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"PID controller; Algorithm; Computer science; Dung beetle; Optimization algorithm; Control theory (sociology); Mathematical optimization; Mathematics; Biology; Control engineering; Engineering; Ecology; Artificial intelligence; Control (management); Temperature control","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002629664,0.0004297442,0.000485447,0.0003691972,0.0002646052,0.000553996,0.0006539476,0.0004017454,0.002034869],"category_scores_gemma":[0.0003862681,0.0001680066,0.000275334,0.0002667923,0.0002272092,0.0003077812,0.0003543411,0.0003955005,0.0003793567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816452,"about_ca_system_score_gemma":0.0004912965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003553517,"about_ca_topic_score_gemma":0.00304919,"domain_scores_codex":[0.9998356,0.00002934475,0.000009869283,0.00003602546,0.00007379098,0.00001534485],"domain_scores_gemma":[0.9998811,0.00002413328,0.00002069377,0.00001309793,0.00005285157,0.000008074191],"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":[0.0001552243,0.00008217587,0.001385794,0.0002045548,0.00007676906,0.0001490508,0.0001095686,0.68942,0.03607931,0.006931573,0.003904651,0.2615013],"study_design_scores_gemma":[0.00001683159,0.00002424954,0.0002796403,0.000004941481,0.000005917784,0.00002562637,0.000005502467,0.9958493,0.001739407,0.0002566364,0.00178637,0.00000558007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03304113,0.0004175824,0.9562719,0.0001277862,0.00008291104,0.00006834042,0.00003306981,0.0008706767,0.009086591],"genre_scores_gemma":[0.7555243,0.0002607082,0.2366114,0.000105164,0.00004361859,0.0001717162,0.0001203005,0.00008757682,0.007075309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003553517,"threshold_uncertainty_score":0.007065654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005548958462725123,"score_gpt":0.1773201681183363,"score_spread":0.1717712096556111,"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."}}