{"id":"W2050732669","doi":"10.2495/ws130161","title":"Optimal operation of water pumping stations","year":2013,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Electricity; Reliability (semiconductor); Energy consumption; Scheduling (production processes); Differential evolution; Total cost; Water supply; Mathematical optimization; Reliability engineering; Environmental science; Engineering; Environmental engineering; Power (physics); Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003786828,0.0005463892,0.0005654427,0.0005410201,0.000462065,0.001058915,0.0005780942,0.000708961,0.002038506],"category_scores_gemma":[0.001350649,0.0004402463,0.0003585002,0.0004789286,0.0003713966,0.0007915421,0.0005195356,0.0004318303,0.0002244904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008705254,"about_ca_system_score_gemma":0.001616732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004538619,"about_ca_topic_score_gemma":0.005655075,"domain_scores_codex":[0.9997733,0.00005111867,0.00001171464,0.00006154655,0.00004492162,0.00005736715],"domain_scores_gemma":[0.9997637,0.00009813692,0.00005116607,0.00001558276,0.00005066579,0.00002085249],"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":[0.0001052432,0.00004609925,0.001254648,0.0000725037,0.00001957302,0.00007476035,0.00005224831,0.9489521,0.006514374,0.006561577,0.0005850925,0.0357618],"study_design_scores_gemma":[0.00002496434,0.00006488417,0.0004034593,0.000008082867,0.00001252427,0.00001614878,0.00003917757,0.9931737,0.002769914,0.002752473,0.0007259072,0.000008681945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2483303,0.0004948152,0.7332352,0.0002969622,0.00006676531,0.0002049085,0.0002217119,0.0004240815,0.01672526],"genre_scores_gemma":[0.9092371,0.0001927516,0.08837633,0.00003363173,0.000009304558,0.00009162983,0.00008594275,0.00002465965,0.001948679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004538619,"threshold_uncertainty_score":0.009024441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003386997626334863,"score_gpt":0.1444101308207079,"score_spread":0.141023133194373,"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."}}