{"id":"W2974234787","doi":"10.1016/j.scs.2019.101848","title":"Forecast of urban water consumption under the impact of climate change","year":2019,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Water resources management and optimization","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Climate change; Precipitation; Consumption (sociology); Linear regression; Bayesian probability; Climatology; Water supply; Regression analysis; Econometrics; Meteorology; Statistics; Mathematics; Geography; Environmental engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001044039,0.00006536552,0.00009416694,0.00001716653,0.00004502064,0.00002152284,0.00004156126,0.00003323977,0.00008440754],"category_scores_gemma":[3.451702e-7,0.00003774164,0.00007962123,0.00003168088,0.00004820846,0.0001260123,0.00004727496,0.00003546146,0.00000147628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003091924,"about_ca_system_score_gemma":0.000001685198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007990686,"about_ca_topic_score_gemma":5.660754e-7,"domain_scores_codex":[0.9996151,0.000007300517,0.00008870981,0.00004912592,0.00005233227,0.0001873903],"domain_scores_gemma":[0.9998432,0.000008832583,0.00001986156,0.00008191526,0.00003440706,0.00001174861],"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":[0.00009962071,0.00007007297,0.2543806,0.01402947,0.001001928,0.000002147268,0.1892594,0.4854948,0.0008939324,0.04520792,0.00752078,0.002039384],"study_design_scores_gemma":[0.002406013,0.0004447155,0.1457519,0.000151607,0.0001633789,0.000002553734,0.23782,0.6018076,0.002867784,0.003398182,0.004518234,0.0006680632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969509,0.000271504,0.0002207341,0.00002009234,0.00002408768,0.0002249684,0.000004967927,0.000022952,0.002259817],"genre_scores_gemma":[0.9981908,0.0008522813,0.00002106287,0.00001166294,0.00002162561,0.000007700224,0.00001488524,0.00001013549,0.0008698021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1163128,"threshold_uncertainty_score":0.1539059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0097701602220379,"score_gpt":0.2042687904105349,"score_spread":0.194498630188497,"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."}}