{"id":"W3080584630","doi":"10.4236/jpee.2020.88003","title":"Hydropower Production Optimization from Inflow: Case Study of Songloulou Hydroplant","year":2020,"lang":"en","type":"article","venue":"Journal of Power and Energy Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Hydropower; Particle swarm optimization; Inflow; Production (economics); Mathematical optimization; Dynamic programming; Computer science; Linear programming; Nonlinear programming; Nonlinear system; Operations research; Engineering; Mathematics; Economics; Geography","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":[0.00005791398,0.0001170998,0.0001892609,0.0001239953,0.00001914071,0.00002636333,0.00005919865,0.00003616692,0.00001786833],"category_scores_gemma":[0.00001636082,0.0001075093,0.00003383247,0.000151556,0.00000565086,0.0002340489,0.00002354291,0.00009321926,2.135396e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001575374,"about_ca_system_score_gemma":0.000002580482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002332586,"about_ca_topic_score_gemma":0.000003607829,"domain_scores_codex":[0.9993432,0.00001013909,0.0003311192,0.00008536987,0.0001375451,0.00009266687],"domain_scores_gemma":[0.9997232,0.000009407995,0.00007969209,0.00006844487,0.00004196487,0.0000773214],"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":[0.00001564982,0.00003008846,0.0002817404,0.00002532961,0.0001188172,0.0002321813,0.002678291,0.9950212,0.001192203,0.00000307311,0.0001147901,0.0002866129],"study_design_scores_gemma":[0.000527047,0.0002210916,0.00008100237,0.00003582877,0.00007554598,0.0001541454,0.0008200181,0.9949751,0.001520313,0.00000119112,0.001450867,0.0001378335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.871533,0.0002436147,0.1276673,0.00003855167,0.0003533635,0.00003212835,0.000001017568,0.00004404296,0.00008690826],"genre_scores_gemma":[0.9978434,0.00009324253,0.001837934,0.00001221911,0.0001750983,9.88377e-7,0.000002687174,0.00002497063,0.000009480621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1263103,"threshold_uncertainty_score":0.4384102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005683803055283718,"score_gpt":0.1645690565942044,"score_spread":0.1588852535389207,"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."}}