{"id":"W3210924576","doi":"","title":"Modelling Phosphorous Dynamics in a Wastewater Treatment Process using Bayesian Optimized LSTM","year":2021,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kruger (Canada)","funders":"","keywords":"Bayesian probability; Dynamics (music); Wastewater; Process (computing); Process dynamics; Environmental science; Process engineering; Computer science; Sewage treatment; Artificial intelligence; Biological system; Biochemical engineering; Environmental engineering; Engineering; Physics; Biology","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":[0.00007626112,0.0002381817,0.000289078,0.00003198315,0.00005502277,0.00003984959,0.0001918773,0.0001142658,0.00005864106],"category_scores_gemma":[0.00002121802,0.0002267304,0.00007871708,0.000320618,0.00004219464,0.00009694121,0.0003406719,0.0001863988,0.00001942977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191554,"about_ca_system_score_gemma":0.0000140479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007874936,"about_ca_topic_score_gemma":0.000001813329,"domain_scores_codex":[0.9985979,0.00001446061,0.0002704762,0.0004646314,0.0001890273,0.0004634922],"domain_scores_gemma":[0.9995584,0.00005303036,0.00003492834,0.0002046883,0.000006811289,0.000142181],"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":[0.00001101199,0.00007813441,0.0001959785,0.00001393095,0.000009653068,0.0001224969,0.0002544482,0.9799726,0.01832383,0.000004960848,0.000001957701,0.001011019],"study_design_scores_gemma":[0.0004765982,0.0000178496,0.000002293051,0.0000746889,0.00001120139,0.00006099521,0.000008235147,0.9566895,0.04232094,0.00006068844,0.00003503508,0.000241992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7549247,0.00002516939,0.2446334,0.00005308081,0.00009786164,0.0000790661,0.000001762078,0.00009669636,0.00008822173],"genre_scores_gemma":[0.8370879,0.000002160623,0.1627834,0.00003590139,0.00003018781,0.000007987057,0.00001315914,0.00002847912,0.00001079175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08216318,"threshold_uncertainty_score":0.9245796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323797244743977,"score_gpt":0.2094156358640411,"score_spread":0.1961776634166013,"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."}}