{"id":"W2799991455","doi":"10.21967/jbb.v3i2.112","title":"Soft-sensing modeling of chemical oxygen demand in photo-electro-catalytic oxidation treatment of papermaking wastewater","year":2018,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Papermaking; Chemical oxygen demand; Wastewater; Mean squared error; Artificial neural network; Multilayer perceptron; Biochemical oxygen demand; Approximation error; Environmental science; Perceptron; Levenberg–Marquardt algorithm; Coefficient of determination; Computer science; Backpropagation; Engineering; Pulp and paper industry; Environmental engineering; Mathematics; Algorithm; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003389051,0.0005583214,0.000488378,0.0002810996,0.0001810854,0.0005923762,0.000644792,0.0009629091,0.0004267276],"category_scores_gemma":[0.0005771586,0.0003160745,0.000709758,0.0002547216,0.0002677622,0.0004979551,0.0003247252,0.0004875452,0.00008823954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006067838,"about_ca_system_score_gemma":0.0004527237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01375592,"about_ca_topic_score_gemma":0.006391532,"domain_scores_codex":[0.9998602,0.00002493193,0.00001194288,0.00004676258,0.00003761824,0.00001854373],"domain_scores_gemma":[0.9998066,0.0001088287,0.0000260773,0.000008850847,0.00004394001,0.000005731855],"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":[0.00004869436,0.00005957244,0.001547223,0.00007109341,0.00002142911,0.00005847901,0.00003472282,0.9834968,0.008610676,0.0002214563,0.0000586799,0.005771198],"study_design_scores_gemma":[9.222438e-7,0.00000973396,0.0002542779,0.000001096027,0.000001874407,0.000002124192,0.000003305023,0.9987549,0.0009080406,0.00004301978,0.00001874762,0.000001997655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8055974,0.0007714033,0.1887638,0.000188798,0.00005008076,0.00008683187,0.0001877627,0.0002410543,0.004112874],"genre_scores_gemma":[0.9939984,0.0001610977,0.004304815,0.00001704297,0.000003016649,0.00005250856,0.00005814136,0.000005400263,0.001399654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01375592,"threshold_uncertainty_score":0.02735168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01920605620810276,"score_gpt":0.2379299201258766,"score_spread":0.2187238639177738,"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."}}