{"id":"W2331630080","doi":"10.1016/j.biortech.2016.04.021","title":"Heavy metals removal from wastewater using extracellular polymeric substances produced by Cloacibacterium normanense in wastewater sludge supplemented with crude glycerol and study of extracellular polymeric substances extraction by different methods","year":2016,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":161,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extracellular polymeric substance; Wastewater; Chemistry; Glycerol; Extracellular; Extraction (chemistry); Heavy metals; Pulp and paper industry; Waste management; Sewage treatment; Environmental chemistry; Chromatography; Organic chemistry; Biochemistry; Bacteria; Biology; Biofilm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003487628,0.0006851673,0.0008772481,0.0003518374,0.0001657398,0.000045836,0.0004615376,0.0002839999,0.0004702804],"category_scores_gemma":[0.00001176683,0.0004260422,0.00008378736,0.000668796,0.0007136537,0.0003737849,0.0002500365,0.0002293816,0.0000121753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002185976,"about_ca_system_score_gemma":0.00001042436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521196,"about_ca_topic_score_gemma":0.0003179153,"domain_scores_codex":[0.9959735,0.0004611156,0.0008903173,0.001269099,0.0005134758,0.0008924961],"domain_scores_gemma":[0.9984341,0.00009366561,0.0005108026,0.000788069,0.00001733526,0.0001560613],"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":[0.000914752,0.0009217219,0.1021044,0.00001249567,0.0001639216,0.000101772,0.0007059863,0.00000316275,0.8911915,0.000001393525,0.00002580567,0.003853047],"study_design_scores_gemma":[0.003462502,0.001113586,0.003153355,0.00005328739,0.0002175745,0.00009780315,0.002178491,0.00008466654,0.9880349,0.00002198346,0.001030481,0.0005513436],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906079,0.007153125,0.0004231649,0.000343191,0.00009144452,0.001138524,0.00009785774,0.0001397543,0.000005109913],"genre_scores_gemma":[0.9932117,0.0001074404,0.006014966,0.000007769239,0.00002445988,0.0000978327,0.00004914315,0.00007742955,0.0004092008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09895106,"threshold_uncertainty_score":0.9998192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261186267419038,"score_gpt":0.2492059096435562,"score_spread":0.2365940469693658,"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."}}