{"id":"W2948260724","doi":"10.1039/c9em00094a","title":"Response of sulfate-reducing bacteria and supporting microbial community to persulfate exposure in a continuous flow system","year":2019,"lang":"en","type":"article","venue":"Environmental Science Processes & Impacts","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Wilfrid Laurier University; Regional Municipality of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Persulfate; Sulfate; Environmental chemistry; Biodegradation; Sulfate-reducing bacteria; Bacteria; Environmental science; Microbial population biology; BTEX; Continuous flow; Chemistry; Benzene; Biology; Ethylbenzene; Biochemistry; Biochemical engineering; Organic chemistry; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00216339,0.000161518,0.0002093532,0.0001155785,0.0002250082,0.00006233782,0.0003227276,0.00004938459,0.0003592255],"category_scores_gemma":[0.0003126189,0.0001549839,0.00002201108,0.0004951214,0.0004208757,0.0007735282,0.0004079402,0.0001482582,0.0001051271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005648582,"about_ca_system_score_gemma":0.00007375267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004630312,"about_ca_topic_score_gemma":0.000128493,"domain_scores_codex":[0.998302,0.0001662395,0.0003605523,0.0003550142,0.0003886432,0.0004275274],"domain_scores_gemma":[0.9992151,0.0001067764,0.0001968502,0.0002514151,0.00000733971,0.0002225198],"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":[0.0001508304,0.00006813269,0.09202276,0.00004916747,0.000001478785,0.000002491785,0.01341843,0.0003173826,0.8906446,0.000001492528,0.000006802321,0.003316454],"study_design_scores_gemma":[0.0006503373,0.0003378009,0.7412044,0.0001567817,0.000005419964,0.00003602875,0.01435643,0.0006446462,0.2423064,0.000003363802,0.00008776523,0.0002106221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987962,0.00001674198,0.0000293971,0.0001192883,0.00007650693,0.0004930993,0.00002023058,0.00002120256,0.0004273732],"genre_scores_gemma":[0.9990069,0.000005020818,0.0007202242,0.00008733085,0.000005389409,0.00001005268,0.000006020799,0.0000113237,0.0001477092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6491816,"threshold_uncertainty_score":0.6320059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006013363463349243,"score_gpt":0.2237191120839938,"score_spread":0.2177057486206446,"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."}}