{"id":"W2065495772","doi":"10.1080/00221686.2008.9521858","title":"Optimal design and operation of irrigation pumping stations using mathematical programming and Genetic Algorithm (GA)","year":2008,"lang":"en","type":"article","venue":"Journal of Hydraulic Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Operating cost; Computer science; Genetic algorithm; Scheduling (production processes); Computation; Selection (genetic algorithm); Nonlinear programming; Lagrange multiplier; Capital cost; Nonlinear system; Mathematics; Algorithm; 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":[0.0008001896,0.0007602967,0.0008766183,0.0007214824,0.0003218404,0.00109536,0.0007182994,0.001069593,0.001315723],"category_scores_gemma":[0.001912421,0.0007448785,0.0007110498,0.0009142996,0.0008093501,0.0006973965,0.0005233455,0.0005766173,0.000219617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107964,"about_ca_system_score_gemma":0.002403025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006833395,"about_ca_topic_score_gemma":0.006093341,"domain_scores_codex":[0.9995514,0.0002052343,0.00002126382,0.00008227315,0.00008504952,0.00005487749],"domain_scores_gemma":[0.9996234,0.0002257148,0.00007376948,0.00001803009,0.00004539045,0.00001359397],"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":[0.00001667193,0.00001306045,0.0002033016,0.00002755453,0.00001181879,0.00001522636,0.0000138556,0.9853796,0.000677994,0.002954081,0.0001472982,0.01053949],"study_design_scores_gemma":[0.00001670574,0.00002740336,0.00008886145,0.000007205658,0.000009188523,0.000007919024,0.000008508457,0.9965631,0.0004878023,0.002230375,0.0005479048,0.000004926637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0431135,0.0003544127,0.9509561,0.0002388588,0.00002703961,0.0001483487,0.00006692859,0.0002860753,0.004808762],"genre_scores_gemma":[0.5457135,0.0006560623,0.4503027,0.00009296452,0.00002141386,0.0005298018,0.0001244953,0.00005540323,0.002503635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006833395,"threshold_uncertainty_score":0.01358724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09022741923866252,"score_gpt":0.3235501843682656,"score_spread":0.2333227651296031,"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."}}