{"id":"W2004689154","doi":"10.1016/j.proeng.2014.02.064","title":"Integrating Data for Water Demand Management","year":2014,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministry of Environment","keywords":"Sustainability; Database; Computer science; Class (philosophy); Data access; Data management; Data science","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":[0.0001974614,0.0001364048,0.0001028872,0.0000976948,0.00003482769,0.00007169197,0.0002858298,0.00003144237,0.00000850136],"category_scores_gemma":[0.00001924743,0.0001124087,0.00002012587,0.0000632315,0.000003603348,0.0002107452,0.0001177181,0.00005609922,0.00001715775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000176778,"about_ca_system_score_gemma":4.465408e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.125161e-7,"about_ca_topic_score_gemma":8.251232e-7,"domain_scores_codex":[0.9993238,0.000002203332,0.0001485027,0.0001818364,0.0000790159,0.0002646358],"domain_scores_gemma":[0.9996406,0.00001714371,0.000008371586,0.0002820144,0.00001287709,0.00003900101],"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":[0.00000295002,0.000005303018,0.00006516328,0.001052749,0.00006482703,6.390165e-7,0.0002314048,0.9877803,0.001126885,0.001405676,0.001495884,0.006768198],"study_design_scores_gemma":[0.0002094495,0.000008046984,0.00002840546,0.00003547109,0.00002612271,5.937924e-7,0.0000154591,0.9138961,0.003197251,0.00006825833,0.082356,0.0001588414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009421823,0.00006468668,0.985762,0.00003111413,0.0002967574,0.0003226909,0.000004260207,0.0006337682,0.003462885],"genre_scores_gemma":[0.9110931,0.00002980307,0.087686,0.0000247157,0.0003529377,0.0001240274,0.0003409361,0.00008332286,0.0002651732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9016713,"threshold_uncertainty_score":0.4583894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008885369615353411,"score_gpt":0.1812557462404944,"score_spread":0.172370376625141,"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."}}