{"id":"W2891152661","doi":"10.1007/s11356-018-3202-9","title":"Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective","year":2018,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Fundamental Research Funds for the Central Universities; Key Laboratory of Bioorganic Chemistry and Molecular Engineering; Ministry of Education of the People's Republic of China; Ministry of Human Resources and Social Security; National Natural Science Foundation of China","keywords":"Tobit model; Data envelopment analysis; Benchmarking; Environmental science; Sewage treatment; Ranking (information retrieval); Regression analysis; Environmental engineering; Environmental economics; Econometrics; Economics; Statistics; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.005849107,0.0002124495,0.0003083331,0.001001855,0.002644608,0.0005529039,0.0005024351,0.00006974954,0.0004444929],"category_scores_gemma":[0.0002042319,0.0001251284,0.00006588773,0.002679646,0.00479748,0.0008705588,0.0003981698,0.00013334,0.00003708679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001979704,"about_ca_system_score_gemma":0.0001856629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690492,"about_ca_topic_score_gemma":0.002337741,"domain_scores_codex":[0.9935484,0.0003936897,0.0003382343,0.001273868,0.003696121,0.000749696],"domain_scores_gemma":[0.9986122,0.0002108944,0.0001376676,0.0005808944,0.0001418971,0.0003164737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006781227,0.001018864,0.1784362,0.00000527727,0.0003378441,0.00007960305,0.03529199,0.003324846,0.69608,0.0005821545,0.00007069702,0.08409441],"study_design_scores_gemma":[0.001147273,0.002991457,0.5021774,0.00007862561,0.0002398038,0.0001078382,0.05053517,0.4208069,0.01975556,0.0006375369,0.000858781,0.0006636695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970523,0.0006135273,0.0006033733,0.0003353551,0.00005035649,0.0002581665,0.00002108091,0.00001142514,0.001054432],"genre_scores_gemma":[0.9986475,0.0001330775,0.0006833014,0.00003353427,0.00005132702,0.000006507147,0.000002283336,0.00000711965,0.0004353172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6763245,"threshold_uncertainty_score":0.9986538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06883423151001694,"score_gpt":0.419391848108051,"score_spread":0.350557616598034,"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."}}