{"id":"W2018672416","doi":"10.1002/1096-9888(200008)35:8<990::aid-jms27>3.0.co;2-k","title":"Characterization of cysteine residues and disulfide bonds in proteins by liquid chromatography/electrospray ionization tandem mass spectrometry","year":2000,"lang":"en","type":"article","venue":"Journal of Mass Spectrometry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Chemistry; Electrospray ionization; Cysteine; Tandem mass spectrometry; Chromatography; Mass spectrometry; Residue (chemistry); Galactosyltransferase; Biochemistry; Enzyme","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":[0.0003407488,0.0002518931,0.0005290631,0.0005312001,0.00008109165,0.00004902695,0.0002799261,0.0002038874,0.0006022421],"category_scores_gemma":[0.00006488777,0.0002439515,0.0001122681,0.001270748,0.00009036487,0.0003762068,0.00002320825,0.0005068624,0.000001586445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001892077,"about_ca_system_score_gemma":0.00006029926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008806149,"about_ca_topic_score_gemma":0.000002679875,"domain_scores_codex":[0.9979624,0.00003880549,0.0009756599,0.0002830452,0.0004077955,0.0003323678],"domain_scores_gemma":[0.9983552,0.00004925874,0.001051572,0.000294371,0.0001291396,0.0001205204],"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":[0.0002024276,0.0001679097,0.007437244,0.00008491064,0.00004352612,0.000009428722,0.00003478266,0.00000740232,0.9910432,0.0005289637,0.00004399552,0.0003962872],"study_design_scores_gemma":[0.0007964495,0.0006699287,0.00308467,0.0002051383,0.00003612634,0.00007710667,0.000031249,0.00005820228,0.990794,0.003359412,0.0006502115,0.0002375171],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9296116,0.0006869003,0.06720956,0.0004114572,0.00001192161,0.0002219852,0.00004528374,0.00004742646,0.001753837],"genre_scores_gemma":[0.9516989,0.003032201,0.04452305,0.00002707192,0.000165944,0.00002595011,0.00005857076,0.00004737478,0.0004209448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02268652,"threshold_uncertainty_score":0.9948055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004404842655522272,"score_gpt":0.2299924525337964,"score_spread":0.2255876098782741,"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."}}