{"id":"W2127996113","doi":"10.1139/cjce-2014-0187","title":"GA–GHCA model for the optimal design of pumped sewer networks","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Benchmark (surveying); Cellular automaton; Cover (algebra); Computer science; Mathematical optimization; Optimal design; Engineering; Algorithm; Mathematics; Mechanical engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006555248,0.0011354,0.001190619,0.0007693164,0.0004353906,0.000859848,0.001498616,0.001417696,0.002908072],"category_scores_gemma":[0.001474109,0.0006202113,0.001096862,0.0007599728,0.0007938762,0.0007911259,0.0006284979,0.001096196,0.0003324369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156052,"about_ca_system_score_gemma":0.001771042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900246,"about_ca_topic_score_gemma":0.01394652,"domain_scores_codex":[0.9995844,0.0001390451,0.0000147365,0.00009999827,0.0001072692,0.00005451593],"domain_scores_gemma":[0.9995053,0.0003008201,0.00003524213,0.00002971656,0.0001083637,0.00002065374],"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":[0.00000682936,0.000003739701,0.00006775435,0.00001497673,0.000008737699,0.00001045065,0.000006571014,0.9945199,0.0002643939,0.001784758,0.0001069864,0.00320489],"study_design_scores_gemma":[0.000003031243,0.000007239647,0.00002157245,0.000002512993,0.000003549977,0.000003493467,0.000001796017,0.9985462,0.00009488105,0.001014091,0.0002997917,0.000001865047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01237328,0.0004320879,0.9826871,0.000109739,0.00005288715,0.00005983716,0.00007650896,0.000241656,0.00396693],"genre_scores_gemma":[0.6899117,0.0007873097,0.3011526,0.0001439447,0.00003998172,0.0005616272,0.0002933798,0.000108209,0.007001115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01900246,"threshold_uncertainty_score":0.03778374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125560790457886,"score_gpt":0.1607848932966125,"score_spread":0.1495292853920336,"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."}}