{"id":"W2066701718","doi":"10.1061/(asce)0733-9496(2002)128:5(370)","title":"Contribution of Neural Networks for Modeling Trihalomethanes Occurrence in Drinking Water","year":2002,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Artificial neural network; Water quality; Environmental science; Logistic regression; Water treatment; Trihalomethane; Water resources; Environmental engineering; Computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003530312,0.00008589934,0.0001943401,0.0002145944,0.0000309199,0.0000397822,0.00006757182,0.00003625196,0.000002827404],"category_scores_gemma":[0.000002500249,0.00005404545,0.00004022603,0.00003849878,0.000007303812,0.0001233249,0.00002252531,0.00007658773,1.558395e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001624328,"about_ca_system_score_gemma":1.403883e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000319645,"about_ca_topic_score_gemma":0.000001627766,"domain_scores_codex":[0.9992364,0.00001918049,0.0003918525,0.00006628855,0.00009758832,0.0001886892],"domain_scores_gemma":[0.9998233,0.00001200533,0.00005037652,0.00004573406,0.00004063511,0.00002800146],"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":[0.00002795889,0.000008278286,0.0009164099,0.0001250026,0.00003773634,0.0000144107,0.002836599,0.9950186,0.00008683544,0.000004193711,0.0001240053,0.0007999673],"study_design_scores_gemma":[0.0007553403,0.0000581515,0.00006365513,0.0002494291,0.00002769053,0.0000157753,0.0001661044,0.9958891,0.0008053397,0.00002424123,0.001866766,0.00007838164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8538608,0.001246184,0.1443812,0.00004461255,0.0002282947,0.0001389176,0.000001102596,0.00001395116,0.00008500851],"genre_scores_gemma":[0.9994281,0.0001065945,0.0003266342,0.000007415843,0.00007727462,0.000007156485,0.000006070081,0.000007312344,0.00003347341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1455673,"threshold_uncertainty_score":0.220391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914931074221407,"score_gpt":0.2140948746804636,"score_spread":0.1949455639382495,"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."}}