{"id":"W4412454847","doi":"10.1016/j.enconman.2025.120185","title":"Intelligent algorithm-assisted short-term load economic distribution at the unit level of a cascaded hydropower station","year":2025,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Term (time); Hydropower; Unit (ring theory); Algorithm; Computer science; Engineering; Environmental science; Electrical engineering; Mathematics; 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":[0.0001032315,0.0001170722,0.0001095682,0.00006242318,0.00009253516,0.0000182673,0.00008696583,0.00004353436,0.0000900347],"category_scores_gemma":[0.000001329911,0.00009787743,0.0000434262,0.00009427006,0.00004406698,0.00005164669,0.0001200815,0.00004450425,0.000005457426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002229677,"about_ca_system_score_gemma":0.000009939375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001444102,"about_ca_topic_score_gemma":0.000211957,"domain_scores_codex":[0.9994168,0.0000206462,0.0001974091,0.0001411934,0.00009094433,0.0001330407],"domain_scores_gemma":[0.9997409,0.00002823444,0.00002908843,0.0001486235,0.00001922887,0.00003395395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007999947,0.00006590281,0.0005103971,0.0003952439,0.0007098703,0.00002603329,0.0004312689,0.04460246,0.001439164,0.04091433,0.03064934,0.880176],"study_design_scores_gemma":[0.000954836,0.00005031694,0.005554506,0.0002140058,0.0001657557,0.000005907356,0.0005817256,0.2685339,0.05523333,0.0001595651,0.6682007,0.0003454035],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4559888,0.002001509,0.4917078,0.0004548184,0.003099621,0.0004173671,0.0002376254,0.0002748996,0.04581752],"genre_scores_gemma":[0.991168,0.001519558,0.0001177183,0.00005886531,0.00001537068,0.00001253194,0.0002140118,0.000009013276,0.006884956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8798306,"threshold_uncertainty_score":0.3991325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360528906585494,"score_gpt":0.2388357530135332,"score_spread":0.2152304639476783,"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."}}