{"id":"W4380785415","doi":"10.1021/acscentsci.3c00294","title":"Absolute Affinities from Quantitative Shotgun Glycomics Using Concentration-Independent (COIN) Native Mass Spectrometry","year":2023,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; University of Alberta","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministry of Advanced Education, Government of Alberta","keywords":"Mass spectrometry; Glycomics; Shotgun; Chromatography; Chemistry; Affinities; Shotgun proteomics; Glycan; Proteomics; Stereochemistry; Biochemistry","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":[0.001126527,0.001316447,0.0009485793,0.001002792,0.0004578958,0.001336806,0.0008869214,0.001161463,0.001337304],"category_scores_gemma":[0.001613895,0.0005249057,0.0004343495,0.0007746613,0.000809708,0.001033555,0.001384976,0.001353618,0.0008336858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105578,"about_ca_system_score_gemma":0.0004602333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009419988,"about_ca_topic_score_gemma":0.001783688,"domain_scores_codex":[0.998245,0.0001732521,0.0001226935,0.000533303,0.0007798825,0.0001458612],"domain_scores_gemma":[0.9990813,0.0002216374,0.0002124485,0.0001718337,0.0002588778,0.00005381893],"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":[0.00008505679,0.00005804806,0.0009377136,0.0001353122,0.00003769544,0.00004949314,0.00004919189,0.0003951012,0.9885052,0.0007793944,0.0004640968,0.008503724],"study_design_scores_gemma":[0.00001074495,0.00009049772,0.003189307,0.00001720523,0.00002453307,0.0002078497,0.00005365091,0.02271487,0.9700762,0.0007702076,0.002809007,0.00003582869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.406285,0.002695062,0.5753784,0.00033244,0.0002888207,0.0004240448,0.002632032,0.004041852,0.00792248],"genre_scores_gemma":[0.6265332,0.002563838,0.3600871,0.0007638065,0.00007250492,0.0008428913,0.00254641,0.0003843153,0.006205941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001337304,"threshold_uncertainty_score":0.00766021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04483874882828222,"score_gpt":0.3273470719677124,"score_spread":0.2825083231394302,"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."}}