{"id":"W4411483525","doi":"10.1016/j.seta.2025.104404","title":"Deep reinforcement learning for methane slip reduction in hybrid-powered liquefied natural gas marine vessels","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Technologies and Assessments","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Government of Canada","keywords":"Methane; Liquefied natural gas; Natural gas; Reinforcement; Slip (aerodynamics); Reduction (mathematics); Environmental science; Engineering; Waste management; Petroleum engineering; Materials science; Composite material; Ecology; Biology; Aerospace engineering; Mathematics","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":[0.0002494359,0.0001562497,0.0001715885,0.0001750165,0.0002288449,0.0000413098,0.0002065173,0.0001031541,0.0001681967],"category_scores_gemma":[0.00007915428,0.0001453117,0.00003932114,0.0004591564,0.0001228004,0.0001954666,0.00039269,0.0001930907,5.231626e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171209,"about_ca_system_score_gemma":0.00003796709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661629,"about_ca_topic_score_gemma":0.00004907988,"domain_scores_codex":[0.9987649,0.00001717449,0.0002415421,0.0003670145,0.0001330137,0.0004764121],"domain_scores_gemma":[0.9996482,0.00003428795,0.00007296285,0.0001945904,0.00002156311,0.00002838088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002905125,0.0002566513,0.01768582,0.0002759456,0.00005988498,0.00008510771,0.0000700094,0.07084235,0.003128982,0.07097073,0.001083555,0.8352504],"study_design_scores_gemma":[0.007189511,0.001730439,0.01736749,0.00033809,0.0001606583,0.00003956313,0.06171479,0.403271,0.09763522,0.1810549,0.2275025,0.001995825],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7778612,0.000657029,0.1577502,0.00226565,0.0003484753,0.0008980897,8.168746e-7,0.000710984,0.05950752],"genre_scores_gemma":[0.9758354,0.0003786086,0.001805028,0.00002484135,0.000004519803,0.0001747865,0.00003340647,0.000009014304,0.02173439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8332546,"threshold_uncertainty_score":0.592564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004352767254312918,"score_gpt":0.2486672335541456,"score_spread":0.2443144662998327,"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."}}