{"id":"W3201357788","doi":"10.1016/j.epsr.2021.107584","title":"Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam","year":2021,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Hydroelectricity; Subspace topology; Ensemble learning; Spillway; Engineering; Generalization; Computer science; Artificial intelligence; Mathematics; Geotechnical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001113953,0.0005835518,0.0007666726,0.0005489251,0.0003204079,0.0006018179,0.0005294422,0.0005476304,0.0007796138],"category_scores_gemma":[0.002549991,0.0002400478,0.0005931106,0.0006248117,0.0001466198,0.0009568457,0.0003815281,0.0009793153,0.0001153046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003762483,"about_ca_system_score_gemma":0.0004082797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009361141,"about_ca_topic_score_gemma":0.006751884,"domain_scores_codex":[0.9997486,0.00006982404,0.00002153074,0.00006761321,0.00004488741,0.00004744615],"domain_scores_gemma":[0.9987748,0.0007581466,0.00009867206,0.00007324451,0.0002461905,0.00004888391],"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":[0.000110861,0.00009405387,0.006985138,0.00002900755,0.00008205706,0.00007475536,0.00002750647,0.9541355,0.0007914629,0.0006719746,0.0008245165,0.03617312],"study_design_scores_gemma":[6.882454e-7,0.000005444405,0.0004601228,0.000001082729,0.000004706571,0.000002436382,0.000003795562,0.9992346,0.0001120053,0.0001480203,0.00002559078,0.00000158319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7458653,0.0007517498,0.2499248,0.0004387897,0.0002234487,0.00002543794,0.0004256231,0.0003657267,0.001979113],"genre_scores_gemma":[0.9923367,0.000128083,0.006769407,0.00001334494,0.00002517769,0.000009835942,0.0002109027,0.00001099509,0.0004955438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009361141,"threshold_uncertainty_score":0.01861334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07279549067791269,"score_gpt":0.3878217865792382,"score_spread":0.3150262959013255,"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."}}