{"id":"W2006702774","doi":"10.1016/j.jenvman.2015.04.023","title":"Anaerobic digestion of thermal pre-treated sludge at different solids concentrations – Computation of mass-energy balance and greenhouse gas emissions","year":2015,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Anaerobic Digestion and Biogas Production","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digestate; Anaerobic digestion; Energy balance; Chemistry; Sewage treatment; Sewage sludge treatment; Greenhouse gas; Thermal energy; Environmental science; Waste management; Energy recovery; Pulp and paper industry; Environmental engineering; Methane; Energy (signal processing); Ecology","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":[0.000207727,0.0003438106,0.0003318843,0.0002168627,0.000302302,0.0004371575,0.000266194,0.0005694784,0.0005797529],"category_scores_gemma":[0.0003438877,0.0002795231,0.0004128054,0.0002475938,0.000225278,0.0002154858,0.0001748005,0.0002610853,0.00007796227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000316666,"about_ca_system_score_gemma":0.0004260571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006940669,"about_ca_topic_score_gemma":0.006382134,"domain_scores_codex":[0.9999449,0.0000136107,0.000005871175,0.00001174386,0.00001274379,0.00001109878],"domain_scores_gemma":[0.9998862,0.00007039565,0.000008393326,0.000008418434,0.00001712183,0.000009517726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00285082,0.0003613983,0.01688031,0.0001980934,0.00008743475,0.0003087153,0.0001063683,0.56439,0.399063,0.0006182092,0.0001146189,0.01502103],"study_design_scores_gemma":[0.00008623664,0.0004142269,0.02362756,0.000008026108,0.00004494939,0.00006750251,0.0000924222,0.8166358,0.1584839,0.0002960825,0.0002219677,0.00002129302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967585,0.00004065757,0.002619782,0.00001152138,0.000004855457,0.000005316285,0.00005172288,0.00001505049,0.0004925536],"genre_scores_gemma":[0.998538,0.0000344624,0.001124421,0.000002010898,0.000001046017,0.000004367715,0.00004813028,0.000003153207,0.0002444297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006940669,"threshold_uncertainty_score":0.0138005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00820088576671674,"score_gpt":0.1957256288289584,"score_spread":0.1875247430622417,"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."}}