{"id":"W1551271092","doi":"","title":"A Longitudinal Study of Water Recycling in Canadian Manufacturing Plants","year":2010,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Construct (python library); Manufacturing sector; Industrial water; Environmental science; Longitudinal data; Econometrics; Volume (thermodynamics); Panel data; Estimation; Economies of scale; Water use; Business; Economics; Environmental economics; Engineering; Computer science; Waste management; Geography; Microeconomics; Ecology; Labour economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631891,0.0003645878,0.0004480294,0.001191561,0.003425399,0.0009747415,0.00129413,0.0008645205,0.002846735],"category_scores_gemma":[0.003754633,0.0004438084,0.0005162299,0.003746155,0.0007378282,0.0005320715,0.0008419148,0.001284117,0.0003944652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01774129,"about_ca_system_score_gemma":0.01827579,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9867306,"about_ca_topic_score_gemma":0.9906447,"domain_scores_codex":[0.9987072,0.0002544952,0.00004789546,0.0001950709,0.0002947948,0.0005005742],"domain_scores_gemma":[0.9962683,0.0005221276,0.0009333087,0.0002399881,0.001296985,0.0007392573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000150295,0.0004537061,0.9839363,0.00002320116,0.00008301731,0.0002037921,0.004455186,0.0007036704,0.0005136641,0.0004650723,0.001850892,0.007161183],"study_design_scores_gemma":[0.00001286818,0.0000912228,0.9907972,0.00001696066,0.00002983222,0.00004266403,0.005239879,0.001825333,0.0001293269,0.00006416055,0.001724288,0.0000262773],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973868,0.00007869732,0.0001549415,0.0002328132,0.000004071427,0.00002833314,0.001468357,0.000004285893,0.0006417672],"genre_scores_gemma":[0.9953023,0.0001681769,0.0003158265,0.0001214279,0.000004982836,0.00003047014,0.002059264,0.000003708347,0.001993808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01774129,"threshold_uncertainty_score":0.1287226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026538086624131,"score_gpt":0.2677290708314105,"score_spread":0.2374636899651691,"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."}}