{"id":"W2036518848","doi":"10.1016/j.jasms.2008.04.033","title":"On the use of DHB/aniline and DHB/<i>N,N</i>-dimethylaniline matrices for improved detection of carbohydrates: Automated identification of oligosaccharides and quantitative analysis of sialylated glycans by MALDI-TOF mass spectrometry","year":2008,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Chemistry; Matrix-assisted laser desorption/ionization; Aniline; Glycan; Mass spectrometry; Dimethylaniline; Chromatography; Analyte; Detection limit; Sample preparation; Analytical Chemistry (journal); Desorption; Organic chemistry; Biochemistry; Glycoprotein","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004744054,0.0009726922,0.0002151682,0.0003397102,0.000242095,0.0004593488,0.0006449761,0.0004259258,0.001113749],"category_scores_gemma":[0.0004245043,0.000456622,0.000194118,0.0001566633,0.0003728592,0.0004018452,0.0003476869,0.0005614258,0.0006739149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271576,"about_ca_system_score_gemma":0.0002291922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006134865,"about_ca_topic_score_gemma":0.001434167,"domain_scores_codex":[0.9996251,0.00008981626,0.00001649586,0.0001056096,0.0001245144,0.00003845109],"domain_scores_gemma":[0.9995993,0.0001396414,0.00009382227,0.00004058556,0.00006996391,0.00005676012],"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":[0.00002015226,0.000005958975,0.00002834043,0.00002315873,0.000002381453,0.00001985041,0.000005814532,0.00001777393,0.9987276,0.0000505403,0.00001434535,0.001084138],"study_design_scores_gemma":[0.000005154996,0.00007279008,0.0005629969,0.000003736192,0.000004907559,0.0002069223,0.000007089181,0.0007613771,0.9971436,0.00002503193,0.001202413,0.000004084963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8675373,0.006593463,0.113585,0.0004001116,0.0001630226,0.0002551833,0.0002149691,0.000640119,0.01061097],"genre_scores_gemma":[0.7865816,0.006451616,0.1985586,0.0003501488,0.00006062559,0.0002156922,0.0005351952,0.0001944655,0.007051987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001113749,"threshold_uncertainty_score":0.003725827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063341176176963,"score_gpt":0.2914920290769826,"score_spread":0.2708586173152129,"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."}}