{"id":"W2663493690","doi":"10.1016/j.wasman.2017.06.021","title":"Hydrothermal post-treatment of digestate to maximize the methane yield from the anaerobic digestion of microalgae","year":2017,"lang":"en","type":"article","venue":"Waste Management","topic":"Algal biology and biofuel production","field":"Energy","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"Royal Golden Jubilee (RGJ) Ph.D. Programme","keywords":"Digestate; Hydrothermal carbonization; Anaerobic digestion; Biogas; Chemistry; Hydrothermal circulation; Methane; Hydrothermal liquefaction; Chemical oxygen demand; Raw material; Waste management; Pulp and paper industry; Wet oxidation; Biofuel; Chemical engineering; Sewage treatment; Carbonization; Organic chemistry; Adsorption","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002079927,0.0001441859,0.0001790824,0.00003099936,0.0002087084,0.00002301613,0.0004412037,0.00005217304,0.00009636374],"category_scores_gemma":[0.00003112855,0.00007437031,0.0000912211,0.00005079871,0.0001556029,0.00005638173,0.0001709602,0.00004898051,0.00004591189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003390505,"about_ca_system_score_gemma":0.000006044229,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009263504,"about_ca_topic_score_gemma":0.00115316,"domain_scores_codex":[0.9991425,0.00008945017,0.0002300323,0.0002436505,0.0001205274,0.0001738398],"domain_scores_gemma":[0.9986824,0.00006505043,0.0002328845,0.0009520864,0.00004203383,0.00002554894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00267346,0.0008216844,0.01249495,0.0001006134,0.00249529,0.00003339112,0.00581742,0.004812731,0.2715077,0.01583617,0.0009741201,0.6824324],"study_design_scores_gemma":[0.001418612,0.001200718,0.4715356,0.0001224282,0.000514855,0.000003507151,0.002087557,0.00006281463,0.4750489,0.001746397,0.04591938,0.000339268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870727,0.0003109717,0.00007988395,0.006922905,0.0003893599,0.0005771746,0.00003343959,0.00001754177,0.004595994],"genre_scores_gemma":[0.9968407,0.0002587037,0.0003241949,0.0001859184,0.0001368224,0.00004095635,0.00003161581,0.00001107468,0.002170053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6820931,"threshold_uncertainty_score":0.9973339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124356488826384,"score_gpt":0.2397410177210633,"score_spread":0.2184974528327995,"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."}}