{"id":"W3033245730","doi":"10.1016/j.biombioe.2020.105602","title":"Optimization of substrate composition in anaerobic co-digestion of agricultural waste using central composite design","year":2020,"lang":"en","type":"article","venue":"Biomass and Bioenergy","topic":"Anaerobic Digestion and Biogas Production","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ontario Ministry of Research, Innovation and Science","keywords":"Response surface methodology; Central composite design; Anaerobic digestion; Mesophile; Manure; Ternary operation; Chemistry; Methane; Mass fraction; Anaerobic exercise; Yield (engineering); Fraction (chemistry); Animal science; Materials science; Analytical Chemistry (journal); Chromatography; Agronomy; Composite material; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001650321,0.0006670465,0.000933888,0.000789346,0.0005679408,0.00146202,0.0005773321,0.0006082229,0.0004323887],"category_scores_gemma":[0.001048131,0.000561223,0.0006272389,0.0005981176,0.0003333458,0.0004267834,0.0005172517,0.0004492576,0.00009228366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007791827,"about_ca_system_score_gemma":0.00128775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344301,"about_ca_topic_score_gemma":0.004218431,"domain_scores_codex":[0.9994399,0.0001906834,0.0000408786,0.00009729899,0.0001598424,0.00007139677],"domain_scores_gemma":[0.9994878,0.0002467502,0.00007490701,0.0000170666,0.0001399455,0.00003345311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002118001,0.001112113,0.002751919,0.000484302,0.00008594362,0.00007683355,0.00008410764,0.7566835,0.1830691,0.0007672167,0.0001550036,0.05261206],"study_design_scores_gemma":[0.000121087,0.001540095,0.002293321,0.00002026303,0.0001092789,0.00003295477,0.00006548614,0.8764269,0.118542,0.0003507279,0.0004693058,0.00002849311],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8923973,0.0006363474,0.1039478,0.00003876435,0.00005151855,0.0001226806,0.0000436641,0.00008271637,0.002679186],"genre_scores_gemma":[0.9720806,0.0001470874,0.02712442,0.00001535264,0.000004529749,0.00008225609,0.00003075788,0.00001588162,0.0004990274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002344301,"threshold_uncertainty_score":0.008727849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281805721255294,"score_gpt":0.2074903112123891,"score_spread":0.1846722539998362,"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."}}