{"id":"W2066307528","doi":"10.1016/j.watres.2010.03.010","title":"Early determination of toxicant concentration in water supply using MHE","year":2010,"lang":"en","type":"article","venue":"Water Research","topic":"Water Systems and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extended Kalman filter; Toxicant; Estimator; Kalman filter; Moving horizon estimation; Water supply; Estimation; Estimation theory; Computer science; Environmental science; Engineering; Environmental engineering; Chemistry; Algorithm; Mathematics; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003309216,0.000211784,0.0003944566,0.0002288499,0.0001695615,0.0003139024,0.0002202302,0.0003761698,0.0009364688],"category_scores_gemma":[0.0005327318,0.0001544862,0.0002421204,0.000191212,0.0002585289,0.000461543,0.0003416284,0.0004222035,0.0003113487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002622328,"about_ca_system_score_gemma":0.0001778096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009942605,"about_ca_topic_score_gemma":0.001768747,"domain_scores_codex":[0.9996852,0.00005963099,0.00001258195,0.00006452322,0.0001385172,0.00003958341],"domain_scores_gemma":[0.9997109,0.0001407467,0.00003188159,0.00002302325,0.0000785985,0.00001474914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004430455,0.0000342136,0.004258324,0.0001104887,0.000009862643,0.00004820374,0.00005032383,0.001010316,0.977246,0.0002460411,0.00009133681,0.01645193],"study_design_scores_gemma":[0.000006463121,0.0002055999,0.004003525,0.000004612364,0.000008409617,0.00002843272,0.00003687951,0.007239724,0.9873284,0.00009126043,0.001039571,0.000007197093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9083683,0.001048074,0.08562288,0.0001824902,0.00006644901,0.0001114634,0.0003332449,0.0002989523,0.003968092],"genre_scores_gemma":[0.9767363,0.000274541,0.01994713,0.00004935076,0.00001239417,0.00003285542,0.00006683611,0.00001088952,0.002869839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009942605,"threshold_uncertainty_score":0.00313282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03648617354645625,"score_gpt":0.2983398349317191,"score_spread":0.2618536613852628,"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."}}