{"id":"W2028049166","doi":"10.1007/s11269-013-0372-6","title":"Multi-Objective Design Optimization of Branched Pipeline Systems with Analytical Assessment of Fire Flow Failure Probability","year":2013,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Sizing; Sorting; Multi-objective optimization; Probabilistic logic; Mathematical optimization; Flow (mathematics); Pareto principle; Optimization problem; Pipeline (software); Selection (genetic algorithm); Conditional probability; Pipeline transport; Reduction (mathematics); Fire protection; Computer science; Engineering; Mathematics; Algorithm; Environmental engineering; Civil 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000274888,0.0001925421,0.0003308394,0.0001122425,0.00003194856,0.00005702471,0.0001470993,0.0000654766,0.00005070989],"category_scores_gemma":[0.000002685929,0.0001248017,0.00004666632,0.0001685126,0.00004189928,0.0001641797,0.00005477681,0.00006789168,0.000004594333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008233055,"about_ca_system_score_gemma":0.000003201811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001162508,"about_ca_topic_score_gemma":0.00001290623,"domain_scores_codex":[0.9986812,0.00009744622,0.0004816916,0.0002357613,0.0002831942,0.0002206693],"domain_scores_gemma":[0.9993703,0.0000134546,0.00008260686,0.000325376,0.0001561942,0.00005200843],"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":[0.00001282367,0.00008120884,0.0007963411,0.0009699311,0.0001708801,0.000001638688,0.0009523846,0.9964138,0.00008382922,0.00001217805,0.0003218681,0.000183135],"study_design_scores_gemma":[0.0006648971,0.00008624025,0.001657997,0.0001604253,0.00007047591,0.000001118604,0.0002840418,0.9959647,0.0007704862,0.000005161899,0.0001777885,0.0001566604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05068664,0.00003115586,0.946548,0.00003269396,0.00006756333,0.001702298,0.000005439982,0.0001030681,0.0008231392],"genre_scores_gemma":[0.8603538,0.000005409652,0.1388907,0.000002643596,0.00002279491,0.0001646047,0.00003058096,0.00002918833,0.0005002469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8096672,"threshold_uncertainty_score":0.5089267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176468368396882,"score_gpt":0.2005961418390551,"score_spread":0.1888314581550863,"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."}}