{"id":"W1978003950","doi":"10.1016/j.chroma.2009.10.015","title":"On-line solid-phase extraction of large-volume injections coupled to liquid chromatography-tandem mass spectrometry for the quantitation and confirmation of 14 selected trace organic contaminants in drinking and surface water","year":2009,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Environment and Climate Change Canada; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Chromatography; Solid phase extraction; Mass spectrometry; Triclocarban; Detection limit; Extraction (chemistry); Simazine; Matrix (chemical analysis); Atrazine; Triclosan; Pesticide","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":[0.0007000539,0.0001367072,0.0002917767,0.000207594,0.00009751658,0.00002070503,0.00008696901,0.0000653015,0.00006829904],"category_scores_gemma":[0.00006591828,0.00009156001,0.00008340177,0.0005794315,0.000115491,0.0003002643,0.00001590143,0.0001526583,0.000001012138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003944035,"about_ca_system_score_gemma":0.000007665486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001558742,"about_ca_topic_score_gemma":0.00004852749,"domain_scores_codex":[0.998747,0.00005311623,0.0005557333,0.0001357284,0.0002736006,0.0002348491],"domain_scores_gemma":[0.9992542,0.0001919397,0.000332516,0.00007797845,0.00003170467,0.0001117101],"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":[0.0005624407,0.0005018479,0.0144767,0.00002112065,0.00004775334,0.00000383019,0.0005458705,0.0006779353,0.9829192,0.00002984744,0.00002560141,0.0001878974],"study_design_scores_gemma":[0.003717189,0.004274137,0.367517,0.0001651174,0.00009977115,0.0001002669,0.0004317542,0.007159172,0.6160235,0.0003304387,0.0000350514,0.0001465992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9652501,0.0001696855,0.0338135,0.0003371031,0.00005679129,0.0003458617,0.000006252044,0.000006178571,0.0000145498],"genre_scores_gemma":[0.9985768,0.0002373249,0.001084488,0.00007195691,0.00001363926,7.508888e-7,0.000002476764,0.000008644654,0.000003920416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3668957,"threshold_uncertainty_score":0.3733709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646023716518473,"score_gpt":0.324784923723553,"score_spread":0.3083246865583683,"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."}}