{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007294145,0.0001616041,0.000611127,0.0003160949,0.00009788006,0.00001435665,0.0002401796,0.0000901871,0.00000429212],"category_scores_gemma":[0.0007162631,0.0001092405,0.0006232536,0.001754534,0.0005441868,0.00002182254,0.00004302884,0.0001468612,2.469505e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004393424,"about_ca_system_score_gemma":0.00008311182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001786863,"about_ca_topic_score_gemma":0.0000153976,"domain_scores_codex":[0.9983625,0.0001180251,0.0008171655,0.0002111032,0.0003098787,0.000181327],"domain_scores_gemma":[0.9962916,0.0004486368,0.002223091,0.0002891593,0.0006950831,0.0000524129],"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":[0.0008948737,0.0001095988,0.001063131,0.00008332307,0.002003867,1.16719e-7,0.0001113586,0.0003572533,0.9951004,0.000039562,0.0001423591,0.00009419779],"study_design_scores_gemma":[0.0007169056,0.002227009,0.007702913,0.00001768318,0.0005829341,0.000005853881,0.0006613742,0.02688855,0.9609548,0.000114191,0.0000294019,0.00009832299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724621,0.0006850346,0.02566192,0.0002549944,0.00002969152,0.0004201379,0.000479833,0.000004701083,0.000001629468],"genre_scores_gemma":[0.986452,0.001181866,0.01221521,0.00003648935,0.00002338513,0.000008370325,0.00003025894,0.00001858599,0.0000338329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0341455,"threshold_uncertainty_score":0.44547,"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."}}