{"id":"W1979181822","doi":"10.1016/j.biortech.2010.04.021","title":"Alkaline extraction of wastewater activated sludge biosolids","year":2010,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Consejo Nacional de Ciencia y Tecnología","keywords":"Extraction (chemistry); Chemistry; Activated sludge; Organic matter; Biosolids; Wastewater; Lysis; Chromatography; Fraction (chemistry); Organic chemistry; Waste management; Biochemistry","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":[0.0002692747,0.0005058091,0.0005162334,0.0003664865,0.0002309742,0.0003419277,0.0002433015,0.0003485664,0.001009056],"category_scores_gemma":[0.0002667072,0.0001634415,0.0003303842,0.0003296882,0.0002097609,0.0003869221,0.0003767399,0.0004205538,0.0004158766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002324518,"about_ca_system_score_gemma":0.0004646338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113794,"about_ca_topic_score_gemma":0.002414804,"domain_scores_codex":[0.9997479,0.00004759948,0.00001497884,0.00002618375,0.00009863313,0.00006459992],"domain_scores_gemma":[0.9998913,0.00003876926,0.00001579577,0.00000809242,0.0000271071,0.00001892287],"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":[0.0001221339,0.00001109169,0.00008818356,0.0000653895,0.000004763241,0.00004765856,0.00003144638,0.00007128462,0.9968532,0.00004700553,0.00002289958,0.002635037],"study_design_scores_gemma":[0.000007569421,0.00009924518,0.001417019,0.000006837593,0.000008408409,0.00006836514,0.00004495055,0.0002604017,0.9968077,0.00005701399,0.001217246,0.00000522991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913572,0.001505013,0.004533051,0.00009880713,0.00005332986,0.00003358424,0.0001612331,0.0000281256,0.00222969],"genre_scores_gemma":[0.9915755,0.001021829,0.001554049,0.00004468374,0.00002008082,0.00001589136,0.0003037434,0.00001863961,0.005445458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00113794,"threshold_uncertainty_score":0.00337559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007501506093312658,"score_gpt":0.2268996697592887,"score_spread":0.2193981636659761,"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."}}