{"id":"W2331258592","doi":"10.1061/41173(414)17","title":"Multi-Objective Design Optimization of Branched Pipeline Systems: Analytical Probabilistic Assessment of Fire Flow Failure","year":2011,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2011","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto","funders":"","keywords":"Sorting; Probabilistic logic; Mathematical optimization; Flow (mathematics); Multi-objective optimization; Pareto principle; Computer science; Probability distribution; Pipeline (software); Engineering; Mathematics; Algorithm; Artificial intelligence; Statistics","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":[0.002105372,0.001134076,0.001042902,0.001096686,0.000495681,0.001071751,0.0008641795,0.001244823,0.001311142],"category_scores_gemma":[0.003311201,0.0007808912,0.000899221,0.0008923928,0.0008234446,0.0009633485,0.0009251539,0.0007402456,0.0001425196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001500278,"about_ca_system_score_gemma":0.001568458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003924326,"about_ca_topic_score_gemma":0.003648924,"domain_scores_codex":[0.9992993,0.0003145275,0.00002015019,0.00006599259,0.000214148,0.00008586945],"domain_scores_gemma":[0.9986803,0.0008855303,0.0001831064,0.00003914052,0.0001612824,0.0000506597],"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":[0.000004285567,0.000003607691,0.00005130315,0.000007985691,0.000004431649,0.000007314119,0.000003567589,0.9979675,0.0001095932,0.0005346436,0.00001410621,0.001291636],"study_design_scores_gemma":[0.000002112057,0.00001217306,0.00003814904,0.000002159497,0.000002226038,0.000003125985,0.000002050492,0.9990203,0.00009443471,0.0007594856,0.0000623046,0.00000140468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06355335,0.000400373,0.9315271,0.0001900826,0.0000194971,0.0000790732,0.00006360367,0.00009377771,0.004073068],"genre_scores_gemma":[0.892354,0.0004695216,0.1038007,0.00004591052,0.00001864367,0.0002365153,0.00008174935,0.00005796034,0.002934952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003924326,"threshold_uncertainty_score":0.01113439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558202438254242,"score_gpt":0.1903432601419076,"score_spread":0.1747612357593652,"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."}}