{"id":"W4388645442","doi":"10.2166/wrd.2023.063","title":"Smart water network infrastructures","year":2023,"lang":"en","type":"article","venue":"Water Reuse","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Multiphysics; Fluent; Environmental science; Water flow; Water consumption; Computer science; Water pressure; Civil engineering; Environmental engineering; Engineering; Simulation; Computer simulation; Finite element method","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009842042,0.0001127098,0.0001086009,0.00006176611,0.00005561023,0.00006173523,0.0001698311,0.00006830855,0.0001519358],"category_scores_gemma":[0.000002510737,0.00006370775,0.00003256752,0.00008221223,0.000008842943,0.0001045342,0.0001020403,0.00006833697,0.001363674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001552955,"about_ca_system_score_gemma":0.000001071419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002273418,"about_ca_topic_score_gemma":0.00002975145,"domain_scores_codex":[0.9992566,0.000013794,0.0001538537,0.0001106979,0.00008599272,0.000379077],"domain_scores_gemma":[0.9996174,0.000003601345,0.000004289007,0.000313976,0.00001409083,0.0000466307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002706158,0.000001247209,0.001152458,0.00004110594,0.00002744623,0.00001223701,0.001869728,0.7462667,0.005853457,0.00002035402,0.2446616,0.00009088714],"study_design_scores_gemma":[0.0005664925,0.00003469012,0.005442608,0.00005853383,0.00002475322,0.00001469638,0.00003800617,0.07160974,0.3056154,0.0039863,0.6119732,0.0006355566],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892156,0.00002827105,0.00164601,0.0002496848,0.00227041,0.0001677862,0.000005047818,0.001526299,0.004890902],"genre_scores_gemma":[0.9950078,0.00001299816,0.0004271609,0.00003766481,0.0004222928,0.00001896412,0.0001446412,0.00004578077,0.003882708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.674657,"threshold_uncertainty_score":0.9994139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006555739723887461,"score_gpt":0.1740812346898468,"score_spread":0.1675254949659593,"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."}}