{"id":"W2889183448","doi":"10.3390/ijerph15091892","title":"Spatial Patterns of Urban Wastewater Discharge and Treatment Plants Efficiency in China","year":2018,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"China Scholarship Council","keywords":"Wastewater; Environmental science; Data envelopment analysis; Sewage treatment; Malmquist index; Inefficiency; Environmental engineering; Pollution; Beijing; Mainland China; Urbanization; China; Productivity; Geography; Economics; Total factor productivity; Economic growth","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":[0.005422663,0.00009249628,0.0002602334,0.001010212,0.0001129403,0.0001558679,0.0005081,0.00003578933,0.0004049383],"category_scores_gemma":[0.0004769872,0.00005921299,0.00005063076,0.0001535779,0.0004043237,0.0003136475,0.0001883271,0.0001881217,0.00001286855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002729504,"about_ca_system_score_gemma":0.0001875804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008026468,"about_ca_topic_score_gemma":0.0004007692,"domain_scores_codex":[0.9958009,0.0004536644,0.0008191832,0.0002463066,0.002330525,0.0003493858],"domain_scores_gemma":[0.9986553,0.000431812,0.0003451036,0.0001557652,0.00009925466,0.0003127792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000148452,0.001233415,0.8562135,0.000002722389,0.00004313167,0.00002839778,0.004008747,0.000004457256,0.001206884,0.000166851,0.0001055371,0.1368379],"study_design_scores_gemma":[0.001185943,0.00263068,0.986483,0.0000569579,0.00000145641,0.0000875764,0.00177677,0.001193824,0.0006932255,0.0008360317,0.004979001,0.00007546639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940639,0.0003064941,0.0004425628,0.004717129,0.0001563416,0.00008845546,0.00008335256,9.967297e-7,0.0001408001],"genre_scores_gemma":[0.9987904,0.0007411154,0.00006404213,0.00005336865,0.0001816118,0.000001152544,0.00000448937,0.000004903884,0.000158924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1367625,"threshold_uncertainty_score":0.443379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003359182040953,"score_gpt":0.4224043576317497,"score_spread":0.3220684394276543,"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."}}