{"id":"W2610365152","doi":"10.1007/s11356-017-9050-1","title":"Total staphylococci as performance surrogate for greywater treatment","year":2017,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Wastewater Treatment and Reuse","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Senter for Internasjonalisering av Utdanning; Norges Miljø- og Biovitenskapelige Universitet; Danmarks Tekniske Universitet; Fogarty International Center; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Greywater; Microbiology; Staphylococcus epidermidis; Enterococcus faecium; Indicator bacteria; Indicator organism; Enterococcus faecalis; Biology; Fecal coliform; Enterococcus; Staphylococcus aureus; Bacteria; Water quality; Ecology; Antibiotics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005057491,0.0005525377,0.0004053096,0.0003319163,0.000189839,0.0006072054,0.0002464369,0.0004952854,0.0008768941],"category_scores_gemma":[0.0005915008,0.0001658375,0.0003758646,0.000326185,0.0002273152,0.0001898254,0.0004040138,0.000495289,0.0002810811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003543318,"about_ca_system_score_gemma":0.0003302884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002182317,"about_ca_topic_score_gemma":0.004473034,"domain_scores_codex":[0.9990128,0.0001990734,0.00006600923,0.0001993723,0.0004484614,0.00007417464],"domain_scores_gemma":[0.999605,0.00009007041,0.0001107223,0.00002964502,0.0001269592,0.00003769205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001067469,0.00005610147,0.002700644,0.0001140849,0.00001110867,0.00001526126,0.00004208014,0.0001990232,0.9933037,0.0000214181,0.00004360974,0.003386189],"study_design_scores_gemma":[0.000006349329,0.001320055,0.01181158,0.00002170801,0.00002769504,0.00004011476,0.00008954661,0.001239328,0.9845546,0.0000329942,0.0008449476,0.00001113764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929568,0.0009657698,0.003886668,0.00005294157,0.00003329009,0.000113532,0.0005654781,0.00005908966,0.001366414],"genre_scores_gemma":[0.980209,0.001395304,0.0137526,0.0001418456,0.00001415579,0.0001500817,0.001068373,0.0000321565,0.003236522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002182317,"threshold_uncertainty_score":0.004339218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04700476332926673,"score_gpt":0.3341633327494276,"score_spread":0.2871585694201609,"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."}}