{"id":"W2080344356","doi":"10.1016/j.biortech.2012.06.075","title":"Bacterial polymer production using pre-treated sludge as raw material and its flocculation and dewatering potential","year":2012,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flocculation; Raw material; Chemistry; Dewatering; Extracellular polymeric substance; Fermentation; Pulp and paper industry; Sterilization (economics); Wastewater; Activated sludge; Food science; Chromatography; Waste management; Bacteria; Biology; Organic chemistry","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":[0.0003435515,0.0007146308,0.0004669348,0.0003238323,0.0002034326,0.000387087,0.0001763847,0.0004792059,0.0005575856],"category_scores_gemma":[0.0005113637,0.0002227481,0.0004947621,0.0004920728,0.0002278783,0.0004571268,0.0002848762,0.0005945539,0.000271887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002611408,"about_ca_system_score_gemma":0.0003215726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009522208,"about_ca_topic_score_gemma":0.001148516,"domain_scores_codex":[0.9997141,0.00005342547,0.00003191191,0.00004651283,0.00008112725,0.00007291276],"domain_scores_gemma":[0.9997395,0.0000805965,0.00004007617,0.00002796557,0.00006049966,0.00005139713],"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":[0.00008708087,0.00002955812,0.000107492,0.00003082385,0.000003696008,0.00002146019,0.00001198958,0.0001071108,0.9986768,0.00002159623,0.000005855492,0.000896507],"study_design_scores_gemma":[0.000002865159,0.0001991594,0.0007750784,0.000002865017,0.000006877093,0.00001671861,0.000008775282,0.000176091,0.9986804,0.00001165897,0.0001169428,0.000002513631],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997348,0.0004662892,0.001478422,0.00002989958,0.00001598605,0.00001662782,0.0001161217,0.00001746181,0.0005112324],"genre_scores_gemma":[0.9955888,0.0005871002,0.002166628,0.00001309141,0.000006260332,0.00002160215,0.0002793497,0.00001750524,0.001319651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009522208,"threshold_uncertainty_score":0.001894712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211965960738547,"score_gpt":0.2366558345314884,"score_spread":0.2245361749241029,"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."}}