{"id":"W4407821405","doi":"10.1016/j.segan.2025.101664","title":"A fully adaptive distributionally robust multistage framework based on mixed decision rules for wind-thermal system operation under uncertainty","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Guangxi Key Research and Development Program; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Mathematical optimization; Robust optimization; Thermal; Computer science; Decision rule; Operations research; Engineering; Mathematics; Meteorology; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001836265,0.000311019,0.0004236847,0.0003346754,0.001026026,0.000601441,0.0003604593,0.000388913,0.00003196457],"category_scores_gemma":[0.0006610556,0.0002376082,0.0001642817,0.0008696135,0.0001194851,0.00030593,0.0001140107,0.0002065556,0.000002149615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000332064,"about_ca_system_score_gemma":0.000357651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002250901,"about_ca_topic_score_gemma":0.00008077493,"domain_scores_codex":[0.9970589,0.0002546677,0.0007006556,0.0007638916,0.0006484134,0.0005734693],"domain_scores_gemma":[0.9954088,0.002669404,0.0002458364,0.0004917028,0.001032809,0.0001513881],"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":[0.0007352737,0.00004261646,0.0002493327,0.000007128026,0.00001892658,0.000008860996,0.00001392145,0.6422493,9.426483e-7,0.3347922,0.002985033,0.01889649],"study_design_scores_gemma":[0.001015639,0.0002156529,0.002998332,0.0001439242,0.00003781483,0.000001210797,0.002118967,0.9598224,0.00001584338,0.01818189,0.01518909,0.0002592782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01418802,0.0005706354,0.9822879,0.0004115587,0.0006466245,0.0004412387,0.0000626053,0.00006947324,0.00132189],"genre_scores_gemma":[0.986505,0.000101083,0.009020775,0.0004443576,0.0003100276,0.0001171634,0.0003335899,0.00002201749,0.003145956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9732672,"threshold_uncertainty_score":0.9689382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780786509536742,"score_gpt":0.2877832225990316,"score_spread":0.2699753575036641,"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."}}