{"id":"W3176188453","doi":"10.1089/ees.2020.0372","title":"Enhanced Coagulation for Removal of Natural Organic Matter and Disinfection Byproducts: Multivariate Optimization","year":2021,"lang":"en","type":"article","venue":"Environmental Engineering Science","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Dissolved organic carbon; Coagulation; Settling; Flocculation; Chemistry; Water treatment; Sedimentation; Natural organic matter; Mixing (physics); Fractionation; Pulp and paper industry; Environmental chemistry; Haloacetic acids; Organic matter; Dewatering; Settling time; Trihalomethane; Environmental science; Environmental engineering; Chromatography; Sediment","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001119358,0.0001024983,0.00008398214,0.00003808767,0.0001142974,0.00002913781,0.00005148053,0.0000262254,0.0003085191],"category_scores_gemma":[0.00003546431,0.0000998615,0.00002382436,0.0002149921,0.0001407792,0.0005145901,0.00007983141,0.00003515154,0.00001933728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001811098,"about_ca_system_score_gemma":0.00000532469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001145616,"about_ca_topic_score_gemma":0.000001336624,"domain_scores_codex":[0.9991918,0.000007962375,0.000131544,0.0003273996,0.0001805338,0.0001607874],"domain_scores_gemma":[0.9997535,0.00001794786,0.00005060277,0.0001293092,0.000004364821,0.00004423927],"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":[0.000006517292,0.00004387946,0.006020299,0.000007066963,0.000003267726,7.143475e-7,0.0001203112,0.1392947,0.8536289,0.000008785586,0.000002727028,0.0008628512],"study_design_scores_gemma":[0.0003777049,0.0000447964,0.3163875,0.000009807964,0.00001621156,0.00002930105,0.00002257524,0.09999916,0.5829226,0.00002122981,0.00003666539,0.0001323996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9109719,0.00003046826,0.08843954,0.00003077395,0.0002197444,0.000158355,0.000003578617,0.00002163161,0.0001239766],"genre_scores_gemma":[0.9842927,0.000007569427,0.0153677,0.00001143854,0.0000176245,0.000009383718,0.00002650733,0.00001053459,0.0002565558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3103672,"threshold_uncertainty_score":0.4072234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00278080609804698,"score_gpt":0.1793077813116347,"score_spread":0.1765269752135877,"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."}}