{"id":"W3013470627","doi":"10.1016/j.watres.2020.115743","title":"Characterization of organic matter by HRMS in surface waters: Effects of chlorination on molecular fingerprints and correlation with DBP formation potential","year":2020,"lang":"en","type":"article","venue":"Water Research","topic":"Water Treatment and Disinfection","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Agència de Gestió d'Ajuts Universitaris i de Recerca; Generalitat de Catalunya; European Commission; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades; Centres de Recerca de Catalunya; Canadian Institute for Advanced Research","keywords":"Chemistry; Dissolved organic carbon; Lignin; Organic matter; Nitrogen; Mass spectrum; Environmental chemistry; Mass spectrometry; Carbon fibers; Total organic carbon; Analytical Chemistry (journal); Chromatography; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001549015,0.00006805973,0.00008306307,0.00005186942,0.00003249851,0.00001908056,0.00004576085,0.00005249118,0.0001224247],"category_scores_gemma":[0.000006528588,0.00004641565,0.00001010387,0.0001592298,0.00005262129,0.0003371005,0.00005744696,0.00009221258,0.0001152537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006280319,"about_ca_system_score_gemma":0.000001905557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008018693,"about_ca_topic_score_gemma":0.00000374894,"domain_scores_codex":[0.9991547,0.00009543706,0.0001322171,0.0001593727,0.0003164042,0.0001418053],"domain_scores_gemma":[0.9998355,0.000007604882,0.00003543567,0.00006988663,0.00001692284,0.00003463363],"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":[0.0001311149,0.00006739362,0.08937683,0.00006763577,0.000003777438,0.000002581503,0.001544868,0.0004528872,0.9081026,0.000001652407,0.00001063135,0.0002380576],"study_design_scores_gemma":[0.0005379225,0.0003160306,0.1207806,0.00002956365,0.000004279626,7.95119e-7,0.00001696179,0.001513678,0.8767146,0.00002988243,0.000009095617,0.00004659901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969136,0.000001502127,0.002141949,0.0004472566,0.00001449118,0.00036688,0.000001643322,0.000006661647,0.0001060061],"genre_scores_gemma":[0.9997619,0.000005539629,0.00002385171,0.00002039479,0.000004072308,0.000008714188,0.0001073078,0.000008575792,0.00005957728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0314038,"threshold_uncertainty_score":0.1892775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009035907358146468,"score_gpt":0.2238200024730806,"score_spread":0.2147840951149342,"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."}}