{"id":"W4396835196","doi":"10.1016/j.scitotenv.2024.173051","title":"Enhancing reclaimed water distribution network resilience with cost-effective meshing","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Industria, Energía y Turismo; Generalitat de Catalunya; China Scholarship Council; Universitat de Girona; Canadian Institute for Advanced Research","keywords":"Resilience (materials science); Network planning and design; Computer science; Baseline (sea); Water supply; Network performance; Risk analysis (engineering); Reliability engineering; Environmental science; Engineering; Environmental engineering; Computer network; Business","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006781025,0.00008347756,0.00007096514,0.00001323151,0.0002133411,0.00006220569,0.0002353766,0.00001790847,0.00001271388],"category_scores_gemma":[0.000005680893,0.00003512635,0.000026844,0.0001857044,0.0002797297,0.0002078124,0.0001040156,0.00009564156,0.00001964209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000193608,"about_ca_system_score_gemma":0.000008580513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001512046,"about_ca_topic_score_gemma":0.000002732868,"domain_scores_codex":[0.9991759,0.00003113135,0.0001175752,0.0001476581,0.0002961613,0.0002315699],"domain_scores_gemma":[0.999677,0.00002638408,0.0000169073,0.0002474246,0.000005211185,0.00002708583],"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":[0.000003117311,0.000002208205,0.000003123144,0.00001384445,0.000007053562,4.030683e-7,0.000573095,0.9567564,0.04192859,0.0001253838,0.00006122763,0.0005256144],"study_design_scores_gemma":[0.00006111887,0.00004007311,0.001463863,0.0002068722,0.00002357878,0.00001557211,0.00008746203,0.3774502,0.6199892,0.0001420825,0.0004101689,0.0001098797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9306017,0.0003148223,0.06540406,0.000383489,0.0009054986,0.001194491,0.000006866404,0.00009077093,0.001098251],"genre_scores_gemma":[0.9992961,0.00001044436,0.000156513,0.000001961456,0.00005474951,0.00005554378,0.000001633283,0.000008354799,0.0004146562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5793061,"threshold_uncertainty_score":0.1640869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003585639098767399,"score_gpt":0.17160525965195,"score_spread":0.1680196205531826,"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."}}