{"id":"W4307352729","doi":"10.1016/j.jenvman.2022.116509","title":"Determining the performance of lignin-based flocculants in improving biosludge dewaterability","year":2022,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; FPInnovations","keywords":"Dewatering; Pulp and paper industry; Flocculation; Lignin; Chemistry; Raw material; Organosolv; Pulp (tooth); Waste management; Sewage treatment; Cellulose; Polyacrylamide; Chemical engineering; Environmental science; Environmental engineering; Organic chemistry; Polymer 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":[0.0008011325,0.000426088,0.0002787441,0.0003078217,0.0002096665,0.0004692213,0.0002357062,0.000479015,0.0004384251],"category_scores_gemma":[0.001365516,0.0001584707,0.0002596123,0.0002604716,0.0001608038,0.0004402297,0.0001911963,0.0004226569,0.0001203577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981958,"about_ca_system_score_gemma":0.0003464821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001502492,"about_ca_topic_score_gemma":0.003331524,"domain_scores_codex":[0.9996138,0.0001017269,0.00003821858,0.00004911951,0.0001143101,0.00008278004],"domain_scores_gemma":[0.9995285,0.0001387144,0.00008445755,0.00002699291,0.000153675,0.0000675469],"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":[0.0002063065,0.0001818205,0.0006764203,0.00005268585,0.00001632345,0.00001321056,0.00001949707,0.0004301874,0.9947318,0.00002780371,0.00001804755,0.003625802],"study_design_scores_gemma":[0.000007200632,0.0006335387,0.001967178,0.000006046897,0.00002392329,0.00001069642,0.00001541353,0.0008038197,0.9963363,0.000006182838,0.0001854514,0.000004162219],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988703,0.0003799806,0.0004406322,0.0000155171,0.000008128647,0.00001526584,0.00004175553,0.000005259242,0.0002231732],"genre_scores_gemma":[0.997431,0.0004357654,0.001460543,0.0000186667,0.000004008965,0.00001246985,0.00008922734,0.000005143123,0.0005431643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001502492,"threshold_uncertainty_score":0.004236817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008508391168099224,"score_gpt":0.2011590972682914,"score_spread":0.1926507061001921,"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."}}