{"id":"W4389262603","doi":"10.1016/j.biortech.2023.130147","title":"Bayesian uncertainty quantification for anaerobic digestion models","year":2023,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Python (programming language); Bayesian probability; Computer science; Uncertainty quantification; Anaerobic digestion; Bootstrapping (finance); Flexibility (engineering); Benchmark (surveying); Machine learning; Data mining; Artificial intelligence; Econometrics; Mathematics; Statistics","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":[0.0002207545,0.0001504934,0.0001644124,0.000596168,0.0002148601,0.00009644633,0.001066947,0.0002442765,0.000002995667],"category_scores_gemma":[0.00008654389,0.0001395983,0.00005612891,0.001991052,0.0001135102,0.0002192528,0.0001852815,0.0001192087,0.00007864532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000040856,"about_ca_system_score_gemma":0.00006149019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001434764,"about_ca_topic_score_gemma":0.00001173922,"domain_scores_codex":[0.9986482,0.00001712753,0.000237419,0.0005386699,0.0001355905,0.0004230117],"domain_scores_gemma":[0.9989433,0.00005813101,0.0001179047,0.0007113428,0.0001111512,0.00005813346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000700849,0.00003077433,0.0002610507,0.00003410237,0.000009291642,0.000004290362,0.0001478859,0.002462875,0.00404304,0.89399,0.001619944,0.09738977],"study_design_scores_gemma":[0.0002390122,0.000133546,0.0006485127,0.00002683321,0.00000612003,0.00001117295,0.00008919364,0.6740049,0.003571376,0.3121434,0.008895589,0.0002304236],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01645049,0.0000717124,0.9709852,0.00972264,0.0001294859,0.000310588,0.000007888191,0.002120434,0.0002016005],"genre_scores_gemma":[0.9680023,0.00002915268,0.03142555,0.00008670352,0.00003200685,0.0001670835,0.00002417372,0.00001461051,0.0002184321],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9515518,"threshold_uncertainty_score":0.5692653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781760362219225,"score_gpt":0.2630317088514593,"score_spread":0.2352141052292671,"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."}}