{"id":"W4362730062","doi":"10.2965/jwet.22-134","title":"Upgrading ADM1 by Addition of Lag Phase Sub-model to Simulate Acidic Inhibition of Methanogenic Reactor","year":2023,"lang":"en","type":"article","venue":"Journal of Water and Environment Technology","topic":"Anaerobic Digestion and Biogas Production","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"Science and Technology Research Partnership for Sustainable Development; Japan Science and Technology Agency; Japan Society for the Promotion of Science; Japan International Cooperation Agency","keywords":"Lag; Anaerobic digestion; Methanogen; Methane; Correlation coefficient; Chemistry; Digestate; Pulp and paper industry; Continuous stirred-tank reactor; Coefficient of determination; Bioaugmentation; Saturation (graph theory); Environmental science; Sequencing batch reactor; Wastewater; Environmental engineering; Microorganism; Mathematics; Engineering","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":[0.0001594675,0.00007715647,0.0001661858,0.0003588485,0.00001435156,0.000002556835,0.00004311533,0.00009650725,0.00002462819],"category_scores_gemma":[0.000008180256,0.00006051509,0.00004058691,0.0001108176,0.00005985595,0.00009604788,0.00002554069,0.0001005054,0.000007388272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003658123,"about_ca_system_score_gemma":0.000002148097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.338506e-7,"about_ca_topic_score_gemma":1.310061e-7,"domain_scores_codex":[0.9994038,0.000008323511,0.0002949219,0.00007749441,0.0001002565,0.0001151419],"domain_scores_gemma":[0.9997958,0.000005487754,0.00007272267,0.00007677397,0.00001214473,0.00003709535],"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":[0.00002422029,0.00003890569,0.00003397804,0.00001302093,0.00003021527,0.000003279229,0.00009438607,0.009424075,0.9846094,0.00005719858,0.0009292298,0.004742151],"study_design_scores_gemma":[0.0004725867,0.0002775231,0.00004007126,0.00002991052,0.00003140411,0.00002328817,0.00007595099,0.001643662,0.994455,0.0006881724,0.002199281,0.00006316937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953523,0.0001128498,0.003661508,0.0006786724,0.00006379301,0.00006006035,0.00001826099,0.00004084523,0.0000117092],"genre_scores_gemma":[0.9985225,0.00103043,0.0003502686,0.000007426856,0.00001951359,0.000002198643,0.00002482187,0.00001048805,0.00003231557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009845641,"threshold_uncertainty_score":0.2467734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033658192139326,"score_gpt":0.2159589566416131,"score_spread":0.2056223747202198,"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."}}