{"id":"W3164578835","doi":"10.1016/j.jenvman.2021.112875","title":"Biogas maximization using data-driven modelling with uncertainty analysis and genetic algorithm for municipal wastewater anaerobic digestion","year":2021,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Anaerobic digestion; Biogas; Principal component analysis; Mean squared error; Maximization; Genetic algorithm; Adaptive neuro fuzzy inference system; Engineering; Computer science; Statistics; Mathematics; Fuzzy logic; Machine learning; Mathematical optimization; Waste management; Chemistry; Artificial intelligence; Fuzzy control system; Methane","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":[0.002164569,0.000995164,0.001679578,0.0008048363,0.0006497728,0.001296271,0.001169107,0.002096224,0.001072106],"category_scores_gemma":[0.005584325,0.001175198,0.001442531,0.0008352848,0.0008501796,0.001025357,0.001370711,0.001536928,0.0001478225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001569946,"about_ca_system_score_gemma":0.002061436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02298728,"about_ca_topic_score_gemma":0.01081386,"domain_scores_codex":[0.9996409,0.0001809265,0.00002108153,0.00005326579,0.00005915574,0.00004462964],"domain_scores_gemma":[0.9972512,0.002266249,0.0001325302,0.00004196812,0.000258644,0.00004927616],"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":[0.00001594281,0.000009606951,0.00007577871,0.000008026287,0.0000104798,0.000007228266,0.000007076638,0.9976869,0.0000923683,0.0004341048,0.00003382166,0.001618621],"study_design_scores_gemma":[0.000002396036,0.000002765595,0.00001218998,0.000001055071,0.000001406144,7.312921e-7,8.866475e-7,0.9996543,0.00004369602,0.0002654035,0.0000139081,0.000001354137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1046025,0.0005148514,0.8912024,0.0004414517,0.00004097527,0.00009349112,0.0001695352,0.0003230774,0.002611637],"genre_scores_gemma":[0.8951021,0.0001747609,0.1022573,0.0001039381,0.00002669796,0.0002498939,0.0002018612,0.00008959629,0.001793902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02298728,"threshold_uncertainty_score":0.04570693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422604606924605,"score_gpt":0.2218812958260527,"score_spread":0.1976552497568067,"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."}}