{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004080978,0.0001522123,0.0001280047,0.00011771,0.0003663294,0.0001801347,0.0004774356,0.00008677784,0.00009800807],"category_scores_gemma":[0.0003987632,0.0001468051,0.00005746174,0.0009314807,0.0005363114,0.00004396023,0.0001942285,0.0001453426,0.00006716709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001237753,"about_ca_system_score_gemma":0.0004843714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002281619,"about_ca_topic_score_gemma":0.0001086506,"domain_scores_codex":[0.9978264,0.00006548496,0.0002108413,0.0005353683,0.0006495503,0.0007123293],"domain_scores_gemma":[0.9991648,0.00005091217,0.0000887787,0.0002832257,0.0002157008,0.0001965745],"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":[0.00005966143,0.00002185355,0.00200728,0.000003650199,0.00002294751,0.00004641821,0.0002964658,0.0009443659,0.9944202,0.001784033,0.0001820828,0.0002110756],"study_design_scores_gemma":[0.0008103211,0.0002178336,0.03714181,0.00002485473,0.00001210228,0.0000210564,0.001889375,0.02890078,0.926815,0.001440384,0.002302591,0.0004238997],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912939,0.0001164676,0.006289439,0.0002599114,0.0003689687,0.0003410285,0.0001035738,0.00003295942,0.001193767],"genre_scores_gemma":[0.9951545,0.0001646868,0.003861403,0.0001026052,0.0001380466,0.00001193903,0.0001571709,0.00001348533,0.0003961926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06760517,"threshold_uncertainty_score":0.5986539,"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."}}