{"id":"W3035135995","doi":"10.1002/eng2.12179","title":"Optimized planning of repair works for pipelines in water distribution networks using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Water Systems and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Ottawa","funders":"Qatar National Research Fund","keywords":"Time horizon; Pipeline transport; Pipeline (software); Reliability engineering; Scheduling (production processes); Total cost; Computer science; Water supply; Risk analysis (engineering); Preventive maintenance; Genetic algorithm; Operations research; Engineering; Mathematical optimization; Operations management; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007738682,0.0009004505,0.00109894,0.001085644,0.0004621838,0.001017309,0.00083978,0.001384177,0.002449666],"category_scores_gemma":[0.001670817,0.0007274454,0.0007775075,0.0008876209,0.0006353644,0.0005888139,0.0004957091,0.0007507438,0.0001513733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002250815,"about_ca_system_score_gemma":0.001878738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03086154,"about_ca_topic_score_gemma":0.01829793,"domain_scores_codex":[0.9997476,0.0001020577,0.000008669399,0.00004518397,0.00003434869,0.00006216067],"domain_scores_gemma":[0.9991578,0.0005974137,0.00008828195,0.00001843257,0.00009279784,0.00004528747],"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":[0.000006196565,0.000005456796,0.0000841689,0.000004244673,0.00000287349,0.000005288636,0.000002713036,0.9988237,0.00004424612,0.0001625099,0.00002945266,0.000829143],"study_design_scores_gemma":[0.000003116985,0.000008972677,0.00004343986,0.000001700058,0.000002473435,0.00000101406,0.00000398494,0.9996673,0.00004052555,0.0001816082,0.00004472784,0.000001102232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3287495,0.0007756153,0.6584229,0.0005237616,0.0000733045,0.0002540647,0.000399323,0.0004249002,0.01037659],"genre_scores_gemma":[0.9280464,0.0002295955,0.06747621,0.00005721203,0.00001172709,0.0002309796,0.0002093294,0.00004442181,0.003694063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03086154,"threshold_uncertainty_score":0.06136376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100007520446339,"score_gpt":0.1989848600627518,"score_spread":0.1879847848582885,"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."}}