{"id":"W4214806825","doi":"10.1007/s10661-022-09874-0","title":"Staged energy and water quality optimization for large water distribution systems","year":2022,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Water quality; Scheduling (production processes); Computer science; Schedule; Hydraulics; Environmental science; Process engineering; Engineering; Operations management","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":[0.0002633458,0.0001123348,0.0001155099,0.00002215802,0.0003351908,0.00006789072,0.00003240902,0.00002970851,0.00002295315],"category_scores_gemma":[4.094436e-7,0.00008770579,0.00001957718,0.0000113181,0.000007759364,0.0001121426,0.00008145609,0.00005924984,2.690609e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002218618,"about_ca_system_score_gemma":0.000001346105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002335631,"about_ca_topic_score_gemma":3.646749e-7,"domain_scores_codex":[0.9992353,0.00005714588,0.0001899813,0.0001645558,0.000148373,0.0002046145],"domain_scores_gemma":[0.9998389,0.00001005578,0.00001827964,0.00008366293,0.000002728076,0.00004632352],"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":[0.00001421367,0.00007743967,0.01226213,0.0002897446,0.00006002263,0.000001927661,0.0005732362,0.972098,0.01352406,0.0002869667,0.0001583132,0.0006539667],"study_design_scores_gemma":[0.002463098,0.0002768359,0.01081874,0.00007493406,0.00005806486,0.00001573179,0.004113054,0.8437968,0.05989913,0.00003231052,0.07766593,0.0007853534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7585197,0.0004793055,0.2388205,0.00003563685,0.001364317,0.0002935842,0.0003144688,0.00009895815,0.00007345571],"genre_scores_gemma":[0.9975195,0.0001409127,0.0005598116,0.00000264092,0.0001417908,0.0002651876,0.001022679,0.00002157432,0.0003259283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2389997,"threshold_uncertainty_score":0.3576538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008972331174197561,"score_gpt":0.2224192797348485,"score_spread":0.2134469485606509,"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."}}