{"id":"W4224212928","doi":"10.3390/w14091328","title":"Development of a Water-Pricing Model for Domestic Water Uses in Dhaka City Using an IWRM Framework","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Sewerage; Water pricing; Externality; Groundwater; Water resource management; Revenue; Water resources; Tariff; Integrated water resources management; Resource (disambiguation); Natural resource economics; Environmental science; Social equality; Equity (law); Business; Environmental economics; Environmental engineering; Economics; Water conservation; Engineering; Finance; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002628724,0.0001392872,0.0001679131,0.0001977928,0.0001299896,0.00003729586,0.0001555077,0.00004213135,0.0001340224],"category_scores_gemma":[0.000001962713,0.00009113995,0.0000335773,0.00005608916,0.00001039828,0.0001679342,0.0001806331,0.0001080266,0.000003991869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000100347,"about_ca_system_score_gemma":0.000003851204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001444076,"about_ca_topic_score_gemma":0.00001146749,"domain_scores_codex":[0.9989747,0.00001839168,0.0003039896,0.0001731855,0.0001501734,0.0003795454],"domain_scores_gemma":[0.9997784,0.000006703448,0.00001309135,0.0001543348,0.00001618762,0.00003125027],"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":[0.00002007195,0.00003665182,0.0003750383,0.0001089568,0.00001975108,0.000002459434,0.02501127,0.9476727,0.02650792,0.000007936826,0.000006721584,0.0002305518],"study_design_scores_gemma":[0.0002606554,0.0000146,0.00003859601,0.0000195026,0.00001465183,8.494886e-7,0.0001909628,0.8578975,0.1400839,0.0006836897,0.0006207179,0.0001743441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7757685,0.000005366276,0.2237691,0.00001774549,0.0000963526,0.000229679,0.000002283045,0.00006567916,0.00004530417],"genre_scores_gemma":[0.9444849,7.425431e-7,0.05512919,0.00002954182,0.0000243083,0.0000709483,0.00009607485,0.0000404866,0.0001238121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1687164,"threshold_uncertainty_score":0.3716579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04100489874665046,"score_gpt":0.2449071887011069,"score_spread":0.2039022899544564,"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."}}