{"id":"W2900617911","doi":"10.1139/cjce-2018-0137","title":"Testing evolutionary algorithms for optimization of water distribution networks","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; CMA-ES; Computer science; Optimization problem; Particle swarm optimization; Evolutionary algorithm; Heuristic; Algorithm; Global optimization; Local search (optimization); Local optimum; Nonlinear system; Metaheuristic; Evolution strategy; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000181052,0.00009701695,0.0001537315,0.000152078,0.0000542809,0.00002372607,0.00008930084,0.0000737966,0.00002471192],"category_scores_gemma":[0.00006630852,0.00008812546,0.00005010976,0.0001522958,0.00001879218,0.0001953219,0.000003719064,0.00007596136,5.526085e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001485632,"about_ca_system_score_gemma":0.00005871982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001048175,"about_ca_topic_score_gemma":0.003385475,"domain_scores_codex":[0.9992741,0.000005549209,0.0003512003,0.00005497154,0.00006970696,0.0002444526],"domain_scores_gemma":[0.9992982,0.00002510175,0.00005567875,0.00006999842,0.0003851053,0.0001659038],"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":[0.00000164607,0.000001249691,0.000187764,0.00004507008,0.00002440781,0.000002131417,0.0000843371,0.9973942,0.0001671795,0.00003051976,0.001914522,0.0001469576],"study_design_scores_gemma":[0.0001852423,0.00006604,0.0004997062,0.0001425964,0.00001767393,0.0000429364,0.000009253071,0.9955541,0.0007019661,0.00001133901,0.00266746,0.0001016891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002177243,0.0003300775,0.9959058,0.00001442974,0.001212706,0.00008896444,0.00002384034,0.00002494188,0.0002220648],"genre_scores_gemma":[0.9881414,0.000003818934,0.01103723,0.000002721267,0.0007272432,0.000003387814,0.00004012065,0.00002851174,0.000015523],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9859642,"threshold_uncertainty_score":0.3593652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00938227456744106,"score_gpt":0.1713617926519405,"score_spread":0.1619795180844995,"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."}}