{"id":"W2921769576","doi":"10.1007/s10287-019-00348-2","title":"Optimized operating rules for short-term hydropower planning in a stochastic environment","year":2019,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Hydropower; Term (time); Mathematical optimization; Tabu search; Scale (ratio); Operations research; Algorithm; Mathematics; Engineering","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":[0.0002705241,0.0001178071,0.0001048056,0.000304439,0.00009232885,0.0001408762,0.0002617854,0.00001497203,0.0000423763],"category_scores_gemma":[0.000003846501,0.0001221013,0.00002468304,0.0001997034,0.0000508288,0.0003168152,0.0001197986,0.00004445738,0.00005377183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001135036,"about_ca_system_score_gemma":0.000003790723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.390766e-7,"about_ca_topic_score_gemma":6.088418e-8,"domain_scores_codex":[0.9989542,0.000006426249,0.0002009174,0.0002817182,0.0003012405,0.000255522],"domain_scores_gemma":[0.9997809,0.00003386933,0.00002055075,0.0001164962,0.00001144343,0.00003672619],"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":[0.00000453821,0.00001443139,0.001081512,0.00004378712,0.000008122111,0.000001922793,0.0001811754,0.9963298,0.00005093553,0.001458928,0.00002885121,0.00079604],"study_design_scores_gemma":[0.0005008928,0.00001583196,0.01196438,0.00005442871,0.000006655803,3.744717e-7,0.00008487701,0.9865767,0.0000137291,0.0005309161,0.00009935212,0.0001518631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.275147,0.00002817273,0.7205721,0.00002310689,0.0001483455,0.0006949322,0.00000172211,0.00007240279,0.00331221],"genre_scores_gemma":[0.9270291,0.000002429146,0.07260725,0.00003085196,0.00001644003,0.0000662786,0.00004079339,0.00001452239,0.0001922916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6518821,"threshold_uncertainty_score":0.4979148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125644155609847,"score_gpt":0.2298904552837135,"score_spread":0.218634013727615,"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."}}