{"id":"W4389275576","doi":"10.1007/s11783-024-1780-y","title":"Online soft measurement for wastewater treatment system based on hybrid deep learning","year":2023,"lang":"en","type":"article","venue":"Frontiers of Environmental Science & Engineering","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Aging","funders":"","keywords":"Convolutional neural network; Computer science; Mean squared error; Artificial intelligence; Artificial neural network; Hyperparameter; Machine learning; Process (computing); Effluent; Deep learning; Engineering; Environmental engineering; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003792685,0.0007592707,0.0007160471,0.0005092628,0.0004790517,0.000707195,0.0008711693,0.0007872757,0.002288621],"category_scores_gemma":[0.0007867699,0.0003034744,0.0003894338,0.0003910683,0.0002613488,0.001261033,0.0009665038,0.0007415783,0.0003826416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005727272,"about_ca_system_score_gemma":0.0006466829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004037019,"about_ca_topic_score_gemma":0.007232028,"domain_scores_codex":[0.9996221,0.00004951244,0.00002162457,0.0001072508,0.0001404904,0.00005900599],"domain_scores_gemma":[0.9996344,0.0001089937,0.00003678031,0.00003900905,0.0001522517,0.00002848544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001101211,0.001039693,0.01556791,0.000391108,0.0002100457,0.0002249406,0.0001476479,0.2531109,0.08665278,0.002772476,0.005600598,0.6331807],"study_design_scores_gemma":[0.000009759231,0.00007397642,0.00102074,0.000003868281,0.00001146253,0.00001497898,0.000008787184,0.9916915,0.00622241,0.0006736124,0.0002589527,0.00001010955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2419167,0.0007040271,0.7478243,0.0005116775,0.0003158631,0.00008825583,0.0002844236,0.002605662,0.005749193],"genre_scores_gemma":[0.9730009,0.00007865373,0.02394227,0.0001329468,0.00003815409,0.00004388662,0.0001160254,0.00002644019,0.002620525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004037019,"threshold_uncertainty_score":0.008027077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826996751214824,"score_gpt":0.2059609908941441,"score_spread":0.1876910233819959,"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."}}