{"id":"W2410619404","doi":"10.1021/acs.analchem.5b01077","title":"Cetyltrimethylammonium Bromide-Coated Fe<sub>3</sub>O<sub>4</sub> Magnetic Nanoparticles for Analysis of 15 Trace Polycyclic Aromatic Hydrocarbons in Aquatic Environments by Ultraperformance, Liquid Chromatography With Fluorescence Detection","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"State Administration of Foreign Experts Affairs; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Chemistry; Detection limit; Solid phase extraction; Tap water; Extraction (chemistry); Adsorption; Sorbent; Bromide; Chromatography; Environmental chemistry; Enrichment factor; Wastewater; Trace Amounts; Inorganic chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000717379,0.0008092942,0.001402929,0.0002901166,0.0001170439,0.00006465376,0.0006241978,0.00057991,0.00001974986],"category_scores_gemma":[0.000644676,0.00079444,0.0005231606,0.002667827,0.0008066226,0.0002145863,0.0001010728,0.0006378859,0.00001557861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004831052,"about_ca_system_score_gemma":0.0002225097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003361316,"about_ca_topic_score_gemma":0.00002586014,"domain_scores_codex":[0.9947683,0.00008790202,0.001618357,0.001223131,0.001138491,0.00116387],"domain_scores_gemma":[0.9969272,0.0006695364,0.0005618704,0.0009840471,0.00002484426,0.0008324831],"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":[0.0007612982,0.0006749536,0.003783372,0.0002724985,0.001069764,0.00001907717,0.0001194322,0.00005912648,0.9917963,5.104429e-7,0.00003866838,0.001404969],"study_design_scores_gemma":[0.002043091,0.0002017877,0.0007982423,0.0001862871,0.002454743,0.0000203532,0.0002998734,0.04329206,0.9497256,0.00008009777,0.00004370479,0.0008541612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955162,0.0004482861,0.003052816,0.00007586286,0.00001892993,0.0002698895,0.0000328041,0.0001114409,0.000473801],"genre_scores_gemma":[0.9981095,0.000114422,0.00114412,0.00003066956,0.0000481907,0.0001796114,0.0002039466,0.00009597879,0.0000736225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04323294,"threshold_uncertainty_score":0.9994506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319066753401773,"score_gpt":0.2341811890727936,"score_spread":0.2209905215387759,"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."}}