{"id":"W4378189481","doi":"10.1016/j.scitotenv.2023.164393","title":"Learning from intermittent water supply schedules: Visualizing equality, equity, and hydraulic capacity in Bengaluru and Delhi, India","year":2023,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Water resources management and optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Erasmus+; Natural Sciences and Engineering Research Council of Canada; Mitacs; European Commission","keywords":"Water supply; Equity (law); New delhi; Census; Inequality; Agricultural economics; Water resource management; Operations management; Business; Economics; Geography; Environmental science; Mathematics; Environmental engineering; Medicine; Population; Environmental health","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":[0.000861196,0.00009761714,0.00009740419,0.00008173843,0.0001587524,0.00007791023,0.0003037593,0.00002286793,0.00001744917],"category_scores_gemma":[0.00001286527,0.00005642302,0.00002082407,0.0001457123,0.0004109213,0.0001687325,0.00175803,0.0001372944,0.00001375607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005954464,"about_ca_system_score_gemma":0.00000174836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000116775,"about_ca_topic_score_gemma":0.000003016279,"domain_scores_codex":[0.999059,0.00005344492,0.000159176,0.0001684684,0.0003144248,0.0002455002],"domain_scores_gemma":[0.9997321,0.0000243284,0.00002919372,0.0001766242,0.000002048632,0.00003565627],"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":[0.000004139308,0.00001187902,0.00333394,0.0000311279,0.00001353813,7.050817e-7,0.007828897,0.9389234,0.04678261,0.0001527566,0.000003968494,0.002913092],"study_design_scores_gemma":[0.0003305057,0.00004694553,0.3043331,0.00008219857,0.0000244197,0.000001649319,0.001472892,0.6380225,0.05332303,0.002108717,0.00005292148,0.0002011598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989436,0.00003934781,0.0001251973,0.0002873287,0.00006282169,0.0001586235,0.000001642365,0.00003197815,0.0003494193],"genre_scores_gemma":[0.9996988,0.00008440555,0.00009619624,0.00001111834,0.00001322838,0.000007041706,0.000002824001,0.000009281019,0.00007716136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3009991,"threshold_uncertainty_score":0.2300864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02839585188920347,"score_gpt":0.222503509902719,"score_spread":0.1941076580135155,"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."}}