{"id":"W4302290837","doi":"10.1002/cjce.24705","title":"Enhancement of biosludge dewatering using proteins through dual conditioning","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; FPInnovations","keywords":"Dewatering; Chemistry; Protamine; Cationic polymerization; Flocculation; Zeta potential; Conditioning; Pulp and paper industry; Polyacrylamide; Chemical engineering; Wastewater; Pulp (tooth); Chromatography; Environmental engineering; Environmental science; Organic chemistry; Biochemistry; Polymer chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002331856,0.0006546922,0.0003256248,0.0002367578,0.0001434672,0.0003696594,0.0002217508,0.0004286747,0.000547894],"category_scores_gemma":[0.0002418191,0.0001848418,0.0003321999,0.0001781957,0.0003012748,0.0005016972,0.0004640091,0.0006095393,0.000217514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002309907,"about_ca_system_score_gemma":0.0001641066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004680837,"about_ca_topic_score_gemma":0.0007244377,"domain_scores_codex":[0.9997993,0.00003073253,0.00001424178,0.00004850785,0.00006566416,0.00004155661],"domain_scores_gemma":[0.9998239,0.00003411918,0.00006309087,0.00001219319,0.00003620116,0.0000306224],"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":[0.00002044659,0.000009690244,0.00004151381,0.00002504137,0.000002978332,0.00001103395,0.000006534618,0.00003418062,0.9990562,0.00001617201,0.000005958576,0.000770257],"study_design_scores_gemma":[0.000001958013,0.0000493806,0.0002810633,0.000001771267,0.000004558598,0.00001709079,0.000003201943,0.0002457714,0.9990972,0.000003675141,0.0002924746,0.00000192267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908765,0.0009632971,0.007297539,0.00006145547,0.00001911749,0.00002984619,0.00003700128,0.00005568079,0.0006595591],"genre_scores_gemma":[0.990571,0.0006582707,0.007098321,0.00004409751,0.000008416294,0.00002326929,0.00006140279,0.00002230493,0.001512917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006546922,"threshold_uncertainty_score":0.001832843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052340510547325,"score_gpt":0.2181876939534525,"score_spread":0.1976642888479792,"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."}}