{"id":"W4413019592","doi":"10.1016/j.ifacol.2025.07.075","title":"Safe Reinforcement Learning-Based Control for Hydrogen Diesel Dual-Fuel Engines","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Alberta","funders":"National Research Council Canada; Mitacs","keywords":"Diesel fuel; Dual (grammatical number); Automotive engineering; Reinforcement learning; Control (management); Engineering; Computer science; Environmental science; Artificial intelligence; Art","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004078316,0.0003901449,0.0003260918,0.0001435756,0.0002421349,0.0004042943,0.0004705599,0.0003178031,0.0009478696],"category_scores_gemma":[0.0008548354,0.0001614275,0.0002067993,0.00007187349,0.0005968624,0.0002087204,0.0005227046,0.000596322,0.0001402663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005932966,"about_ca_system_score_gemma":0.0007865124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006975838,"about_ca_topic_score_gemma":0.004622793,"domain_scores_codex":[0.9998162,0.00004103033,0.000007813335,0.00002576826,0.00007754288,0.00003149134],"domain_scores_gemma":[0.9996619,0.0001749819,0.00005671574,0.00002522739,0.00006022488,0.00002097366],"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":[0.0001382617,0.00006194857,0.0003018293,0.00003691658,0.00000946905,0.00006529185,0.00003711917,0.9704617,0.01043526,0.002297202,0.0001991463,0.01595579],"study_design_scores_gemma":[0.00001055814,0.00004089215,0.00005364852,0.000001352126,0.000001350942,0.000003677637,0.000001935188,0.9971135,0.002247917,0.0003596512,0.0001632509,0.000002270098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2353936,0.0002579205,0.7504799,0.0003904559,0.00008347986,0.0001140361,0.00005362972,0.001420037,0.01180695],"genre_scores_gemma":[0.9877836,0.00002556393,0.01104474,0.0000183272,0.000003123184,0.00003122876,0.00001256127,0.00001363787,0.001067254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006975838,"threshold_uncertainty_score":0.01387048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008605361049807529,"score_gpt":0.251082804147075,"score_spread":0.2424774430972675,"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."}}