{"id":"W1577315848","doi":"10.1002/j.1551-8833.2000.tb09023.x","title":"Conventional and optimized coagulation for NOM removal","year":2000,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"American Water (Canada)","funders":"","keywords":"Coagulation; Turbidity; Chemistry; Water treatment; Total organic carbon; Pulp and paper industry; Activated carbon; Flocculation; Chromatography; Waste management; Environmental engineering; Environmental science; Environmental chemistry; Adsorption; Organic chemistry; Engineering; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001648353,0.00007957151,0.000103373,0.00001756074,0.0001364067,0.00005716207,0.00002669595,0.0000385387,0.001423758],"category_scores_gemma":[0.000007777466,0.00006305151,0.00004594199,0.00007312545,0.00004655811,0.0002462483,0.00001226425,0.00003089027,0.0001943022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002562485,"about_ca_system_score_gemma":0.000001669037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003482385,"about_ca_topic_score_gemma":0.00002390292,"domain_scores_codex":[0.9993473,0.00003860254,0.0001220684,0.0001783289,0.0001374706,0.0001762029],"domain_scores_gemma":[0.9997907,0.00004036916,0.00006922158,0.00005500873,0.000007906476,0.00003679152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004433986,0.0002365122,0.6182749,0.000004326083,0.0001195049,0.000001994896,0.001033022,0.01075931,0.003851054,0.00004136985,0.006081579,0.359153],"study_design_scores_gemma":[0.006093325,0.0005448894,0.9098811,0.00002367586,0.0002271624,0.00001246396,0.0001507168,0.02595518,0.005708889,0.003710321,0.0470274,0.0006648938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946288,0.000004464702,0.001640007,0.0007605683,0.00005560343,0.0002197461,0.000004260357,0.0000425136,0.002643963],"genre_scores_gemma":[0.9902954,0.00001897126,0.003135746,0.0001346892,0.00005763092,0.00003456801,0.0001825232,0.000008857791,0.006131674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3584881,"threshold_uncertainty_score":0.9994891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004267672328133662,"score_gpt":0.2072268554424997,"score_spread":0.2029591831143661,"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."}}