{"id":"W2053947172","doi":"10.1016/j.habitatint.2004.04.002","title":"Understanding urban residential water use in Beijing and Tianjin, China","year":2004,"lang":"en","type":"article","venue":"Habitat International","topic":"Water resources management and optimization","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Beijing; Context (archaeology); Water use; Water supply; China; Consumption (sociology); Socioeconomic status; Business; Geography; Water resources; Water resource management; Socioeconomics; Environmental science; Economics; Environmental engineering; Environmental health; Population","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00006139839,0.00007521318,0.00005523457,0.0001880799,0.00002969483,0.0001532986,0.00007155264,0.00002816841,0.00004865439],"category_scores_gemma":[0.000008006873,0.00006852274,0.00001682609,0.00004146267,0.00001595776,0.0004030947,0.00004299843,0.00006727981,0.00001602467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001624217,"about_ca_system_score_gemma":0.00000145789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002763043,"about_ca_topic_score_gemma":0.0002136197,"domain_scores_codex":[0.9995187,0.000006088446,0.0001243665,0.0001034947,0.000120649,0.0001267081],"domain_scores_gemma":[0.999904,0.000007272109,0.000009226851,0.00004896402,0.000007664838,0.00002285824],"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":[0.00003042056,0.00002585275,0.111427,0.00003528735,0.00007464023,0.00005193148,0.00263881,0.8735567,0.001464333,0.009227213,0.001388599,0.00007918163],"study_design_scores_gemma":[0.01014242,0.0001217229,0.4515724,0.0006642921,0.00007553831,0.0000508505,0.0009243026,0.4831481,0.0106935,0.02975875,0.0111675,0.001680601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272129,0.00002034747,0.0677065,0.0003422718,0.0004402217,0.00008667248,0.000003035738,0.00009624127,0.004091823],"genre_scores_gemma":[0.9986658,0.00001341523,0.0008784086,0.00002527259,0.00009054189,0.000003917762,0.00004985138,0.00001596317,0.0002568055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3904087,"threshold_uncertainty_score":0.2794276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02791158433891959,"score_gpt":0.1981618209999514,"score_spread":0.1702502366610318,"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."}}