{"id":"W2892798016","doi":"10.3390/su10103466","title":"Centralized and Decentralized Approaches to Water Demand Management","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Water resources management and optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for International Governance Innovation; Toronto Metropolitan University; Balsillie School of International Affairs; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Incentive; Productivity; Environmental economics; Game theory; Computer science; Business; Risk analysis (engineering); Economics; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002167931,0.0001367406,0.0001234084,0.00008176716,0.0000851918,0.00008148293,0.000102224,0.0000363336,0.00009745931],"category_scores_gemma":[0.00001085306,0.0001063774,0.00002624818,0.000117897,0.00006597938,0.0001063952,0.0001201515,0.0000375887,0.00002543837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001645671,"about_ca_system_score_gemma":0.00000225849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000710413,"about_ca_topic_score_gemma":0.000007413951,"domain_scores_codex":[0.9990727,0.00003327239,0.0001539852,0.0002301576,0.0001020326,0.0004079069],"domain_scores_gemma":[0.9995994,0.000005183846,0.000006996833,0.0002327274,0.00004736247,0.0001083448],"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":[0.002333731,0.0009215347,0.1844674,0.01550089,0.001859096,0.0001711543,0.08813673,0.3448257,0.0007033894,0.08672104,0.02768969,0.2466697],"study_design_scores_gemma":[0.00621617,0.0003503921,0.1050605,0.00006433813,0.0003339175,0.000005615608,0.003872094,0.1659652,0.02661801,0.04919207,0.6402395,0.002082258],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495825,0.00005623853,0.04276931,0.0008844641,0.0001208991,0.001105121,0.000001235923,0.0003378452,0.005142358],"genre_scores_gemma":[0.9967301,0.00001887796,0.00241861,0.0000482677,0.00004056167,0.00004650741,0.00001128887,0.00001920874,0.0006666117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6125498,"threshold_uncertainty_score":0.4337946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02081156504086302,"score_gpt":0.1988754320814433,"score_spread":0.1780638670405803,"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."}}