{"id":"W2053508782","doi":"10.1061/41203(425)37","title":"Real-Time Water Quality Assessment with Bayesian Belief Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Turbidity; Water quality; Computer science; Environmental science; Process (computing); Data mining; Bayesian probability; Process engineering; Artificial intelligence; Machine learning; Engineering","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":[0.0001457278,0.0001003422,0.0001218079,0.00002325418,0.00002990106,0.00002414194,0.00005944742,0.00005357441,0.0005892916],"category_scores_gemma":[2.390816e-7,0.00005882455,0.00001910127,0.00003812183,0.000008067307,0.0001114163,0.0000131132,0.00005068808,0.00004038444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002805438,"about_ca_system_score_gemma":0.00000331192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004259699,"about_ca_topic_score_gemma":0.0001692096,"domain_scores_codex":[0.9994342,0.0000227508,0.0001667016,0.0001057256,0.00008118148,0.0001894118],"domain_scores_gemma":[0.9997208,0.000004145749,0.00001132477,0.0001867718,0.00002343972,0.00005356755],"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":[0.00004196588,0.0001366816,0.01852185,0.0001782591,0.000310457,0.00002849014,0.004183825,0.9451821,0.002470386,0.006005472,0.02185429,0.001086214],"study_design_scores_gemma":[0.0006346338,0.0001392002,0.02519417,0.00004348678,0.00002783791,0.000008767507,0.0001022078,0.9641778,0.007384656,0.0001023459,0.001614375,0.0005704891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01646051,0.00000397404,0.594802,0.000014148,0.0001385695,0.0001381837,8.146354e-7,0.0004287632,0.3880131],"genre_scores_gemma":[0.9751465,0.000007453248,0.01935703,0.00001200129,0.00006759752,0.00001503965,0.00002223249,0.00002743032,0.00534469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.958686,"threshold_uncertainty_score":0.645233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151203363635175,"score_gpt":0.2110819889021258,"score_spread":0.1959616525386083,"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."}}