{"id":"W2025807076","doi":"10.1016/j.watres.2014.10.057","title":"Enhancing pulp and paper mill biosludge dewaterability using enzymes","year":2014,"lang":"en","type":"article","venue":"Water Research","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Lysozyme; Flocculation; Chemistry; Dewatering; Pulp and paper industry; Paper mill; Pulp mill; Enzyme; Pulp (tooth); Chromatography; Waste management; Biochemistry; Effluent; 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":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002702425,0.0004368573,0.0002278636,0.0001677022,0.0001283056,0.0004271458,0.0001565384,0.0003071741,0.0006523497],"category_scores_gemma":[0.0003165612,0.0001233078,0.0002392277,0.0001930069,0.0001596071,0.0005494934,0.000208446,0.0004287296,0.0001330351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249963,"about_ca_system_score_gemma":0.0002065471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050944,"about_ca_topic_score_gemma":0.001518403,"domain_scores_codex":[0.9998525,0.00002108253,0.00001622876,0.00003298352,0.00003850222,0.0000386549],"domain_scores_gemma":[0.9999018,0.00002958382,0.00001924263,0.0000107676,0.00002407303,0.00001465296],"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":[0.00006762558,0.00003975131,0.0001427279,0.00003050581,0.000004245367,0.00001881421,0.0000149927,0.00009958535,0.996494,0.00004163046,0.000009601597,0.003036418],"study_design_scores_gemma":[0.000002658173,0.00007507566,0.000426256,0.000001374533,0.000004901172,0.000008646896,0.000008414022,0.0001543241,0.9990582,0.00000837018,0.000250528,0.000001321812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958539,0.0006325708,0.002671125,0.00004293276,0.00001022224,0.00001792001,0.00003668715,0.00001644095,0.0007182771],"genre_scores_gemma":[0.9946238,0.0006279201,0.002628853,0.00002390325,0.000003738982,0.000009920331,0.00007057512,0.00001008759,0.002001241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001050944,"threshold_uncertainty_score":0.002182305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0809061523955881,"score_gpt":0.3486812243799192,"score_spread":0.267775071984331,"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."}}