{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001505186,0.0001537093,0.0002728148,0.00005260927,0.00001728705,0.00002101764,0.00004027445,0.0001086256,0.000002989577],"category_scores_gemma":[0.00003488379,0.0001425456,0.00008213567,0.0001455892,0.000005142283,0.0001039126,0.00001926212,0.00009843246,1.690894e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822256,"about_ca_system_score_gemma":0.000005593453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001033981,"about_ca_topic_score_gemma":2.688465e-7,"domain_scores_codex":[0.9989046,0.000004901941,0.0005741597,0.0001811598,0.00008611175,0.0002490961],"domain_scores_gemma":[0.9996923,0.00001883759,0.00005201178,0.0001343841,0.00004066146,0.00006182058],"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":[0.000005306444,0.000003956054,0.0003811597,0.0001352613,0.00002153286,0.00006267459,0.0002034871,0.9982994,0.0004726025,5.314936e-7,0.0001653276,0.0002488034],"study_design_scores_gemma":[0.0002302246,0.00001409935,0.0003701469,0.0002011286,0.00001698611,0.00003506137,0.00001240903,0.9952458,0.003327552,0.000001819031,0.0003781979,0.0001665808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06848066,0.0006685889,0.9296992,0.000007299412,0.0005592688,0.0002682601,0.000003368539,0.0003079654,0.000005335507],"genre_scores_gemma":[0.8828717,0.00001152355,0.1166229,0.000003805542,0.0002918071,0.00003014432,0.0001121664,0.00004978076,0.000006072288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8143911,"threshold_uncertainty_score":0.5812839,"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."}}