{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004293743,0.00009786413,0.0002562517,0.0005134285,0.00004919231,0.00003978582,0.0002104136,0.0001531953,0.00003406219],"category_scores_gemma":[0.00008422382,0.00006704072,0.00003048609,0.0004742785,0.0001063438,0.000222078,0.00008066212,0.0004091136,0.000007708098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002547969,"about_ca_system_score_gemma":0.00002662348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006446117,"about_ca_topic_score_gemma":0.000004998137,"domain_scores_codex":[0.998859,0.00003733542,0.0004302137,0.0001060614,0.0002861256,0.0002812489],"domain_scores_gemma":[0.9992157,0.00002165122,0.00005811232,0.0001163076,0.0004715842,0.0001166056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008078188,0.0004946986,0.03538252,0.00257567,0.0006082879,0.0005138694,0.007497917,0.5814279,0.2071391,0.0008907909,0.1415534,0.02110802],"study_design_scores_gemma":[0.001990935,0.001569294,0.00008731133,0.0002258407,0.00002335312,0.0001898223,0.0008526502,0.2927586,0.6861384,0.0004390605,0.01548986,0.0002348574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8228469,0.003405783,0.1486032,0.0210546,0.0004922932,0.001407106,0.00003976329,0.0003436977,0.00180667],"genre_scores_gemma":[0.9947929,0.000407092,0.004477072,0.00001084717,0.0001369062,0.000006580888,0.000007407717,0.00002231633,0.0001388908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4789993,"threshold_uncertainty_score":0.2733841,"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."}}