{"id":"W3092111695","doi":"10.2166/aqua.2020.147","title":"Lifecycle cost optimization of pipeline projects","year":2020,"lang":"en","type":"article","venue":"Journal of Water Supply Research and Technology—AQUA","topic":"Water Systems and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"HydraTek (Canada)","funders":"National Plan for Science,Technology and Innovation","keywords":"Pipeline (software); Nominal Pipe Size; Range (aeronautics); Pipeline transport; Total cost; Genetic algorithm; Reliability engineering; Engineering; Component (thermodynamics); Operational costs; Flow (mathematics); Computer science; Operations research; Mechanical engineering; Mathematics","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":[0.001305587,0.0005640282,0.000525986,0.001285526,0.0003296036,0.0007310968,0.0004373636,0.0005676635,0.001693927],"category_scores_gemma":[0.002885638,0.0003920973,0.000504685,0.0008902874,0.0004308073,0.0006943227,0.0003982076,0.0003825055,0.0001007861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473607,"about_ca_system_score_gemma":0.001134549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003311374,"about_ca_topic_score_gemma":0.00314003,"domain_scores_codex":[0.9993567,0.0003381037,0.00001527832,0.0000373339,0.0001486609,0.0001039781],"domain_scores_gemma":[0.9990519,0.0006179608,0.00009510353,0.00003847869,0.0001444557,0.00005212356],"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":[0.0000474611,0.00003265256,0.0006513249,0.00003059752,0.000008476955,0.00002807508,0.000009254954,0.988618,0.0006457283,0.001538327,0.0001413377,0.008248776],"study_design_scores_gemma":[0.00001137621,0.0001289979,0.0008741859,0.000007760029,0.000009262647,0.00001491989,0.00002488389,0.9964176,0.0008229878,0.001376294,0.0003051155,0.000006563585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8090333,0.0006119703,0.1760699,0.0003132257,0.00002024746,0.0001754033,0.0002696481,0.0001270895,0.01337917],"genre_scores_gemma":[0.9830116,0.000119302,0.01567239,0.000006201097,0.000002619274,0.00007464697,0.00009788905,0.00001975393,0.000995649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003311374,"threshold_uncertainty_score":0.01069182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03469921188672674,"score_gpt":0.2628385685418896,"score_spread":0.2281393566551629,"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."}}