{"id":"W2386425816","doi":"","title":"Application of Artificial Neural Network in Modeling Paint Waste Water Treated by Coagulation Oxidation Process","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Artificial neural network; Coagulation; Process (computing); Experimental data; Wastewater; Waste management; Environmental science; Backpropagation; Engineering; Process engineering; Environmental engineering; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004356821,0.0006100595,0.0003972567,0.0003831506,0.000285027,0.0005221586,0.0004769133,0.001002602,0.0004410822],"category_scores_gemma":[0.001269605,0.0003040927,0.0003622109,0.0004697856,0.0002255795,0.0006678489,0.0002218213,0.0004157533,0.00009035636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006057203,"about_ca_system_score_gemma":0.0005653091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0130385,"about_ca_topic_score_gemma":0.008331128,"domain_scores_codex":[0.9998332,0.0000527831,0.00001214635,0.00002537962,0.00005485314,0.00002165087],"domain_scores_gemma":[0.9997256,0.0001439513,0.00002804346,0.00001250406,0.00008196387,0.000007866652],"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":[0.00003680687,0.00002946845,0.001204598,0.00002440556,0.00001858295,0.00004997778,0.0000155262,0.9889206,0.001800681,0.0002026766,0.00005317921,0.007643505],"study_design_scores_gemma":[0.0000019238,0.00001490091,0.0002066057,0.000001645548,0.000003463389,0.000003938605,0.000002547195,0.9990307,0.0005668309,0.0001189419,0.00004602292,0.000002576061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.730111,0.0008157152,0.2634832,0.0002720972,0.00007706038,0.00007381848,0.0001609,0.0004384464,0.004567793],"genre_scores_gemma":[0.9883532,0.0002386976,0.0101579,0.00001821986,0.00001123917,0.00005078434,0.00007448956,0.00000926451,0.001086333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0130385,"threshold_uncertainty_score":0.02592522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534840221925325,"score_gpt":0.240281832412454,"score_spread":0.2149334301932007,"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."}}