{"id":"W2974345690","doi":"10.1016/j.talanta.2019.120378","title":"Sorbent and solvent co-enhanced direct analysis in real time-mass spectrometry for high-throughput determination of trace pollutants in water","year":2019,"lang":"en","type":"article","venue":"Talanta","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Sorbent; Chemistry; DART ion source; Dart; Mass spectrometry; Solvent; Detection limit; Analyte; Chromatography; Phthalic acid; Extraction (chemistry); Desorption; Ion-mobility spectrometry; Trace Amounts; Adsorption; Environmental chemistry; Analytical Chemistry (journal); Ionization; Organic chemistry; Ion","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005529375,0.000725763,0.0006365603,0.0005545504,0.0003896561,0.0006631213,0.0007539651,0.0008462092,0.001447966],"category_scores_gemma":[0.0004463835,0.0004706277,0.0004562922,0.0003425277,0.000370851,0.0005295671,0.0008391057,0.000681753,0.001132983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003916634,"about_ca_system_score_gemma":0.0007752237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009312188,"about_ca_topic_score_gemma":0.002651569,"domain_scores_codex":[0.999091,0.0001381044,0.00003420594,0.0002048891,0.0004420636,0.00008967355],"domain_scores_gemma":[0.9998056,0.0000492309,0.00002850413,0.00002061991,0.00007282013,0.0000231862],"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":[0.0001646701,0.00003393338,0.0001365098,0.00007700583,0.00001892256,0.00005129972,0.00001514108,0.0001562379,0.9915668,0.0001454758,0.0001566786,0.007477375],"study_design_scores_gemma":[0.00001752357,0.0001343399,0.0005191682,0.000003359149,0.00001980839,0.0002022062,0.0000107659,0.006127343,0.9904802,0.00004361099,0.00243051,0.00001112802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7881703,0.007319162,0.192473,0.0004807156,0.0006702076,0.0004411972,0.0005331429,0.00190462,0.008007707],"genre_scores_gemma":[0.832098,0.003600657,0.1373236,0.0007150981,0.0001882843,0.0003438886,0.0005995128,0.0002295969,0.02490134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001447966,"threshold_uncertainty_score":0.00484395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007516497452574209,"score_gpt":0.2688332262288471,"score_spread":0.2613167287762729,"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."}}