{"id":"W2059032784","doi":"10.1016/j.aca.2013.05.060","title":"Automation of dimethylation after guanidination labeling chemistry and its compatibility with common buffers and surfactants for mass spectrometry-based shotgun quantitative proteome analysis","year":2013,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Chemistry; Chromatography; Reagent; Mass spectrometry; Proteome; Derivatization; Sample preparation; Isobaric labeling; Shotgun proteomics; Tandem mass spectrometry; Proteomics; Biochemistry; Protein mass spectrometry; Organic chemistry","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":[0.0001665166,0.0001888402,0.0003692081,0.00009566097,0.00009310337,0.00004801764,0.00009778581,0.0001111676,0.0001533398],"category_scores_gemma":[0.000089139,0.0001706067,0.00006812954,0.0004583722,0.00008996913,0.0002227731,0.00002552789,0.000128791,6.220778e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007214594,"about_ca_system_score_gemma":0.00002456757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002497779,"about_ca_topic_score_gemma":0.00001316498,"domain_scores_codex":[0.9988766,0.00001799163,0.000370315,0.0003882007,0.0001691301,0.0001777104],"domain_scores_gemma":[0.9987907,0.0002504321,0.000336752,0.0002837588,0.0002618581,0.00007654465],"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":[0.0001087368,0.00007244851,0.01596101,0.0002860281,0.0001966843,9.114438e-8,0.00006592768,0.00004630984,0.9830197,0.0001783857,0.000002325914,0.00006235595],"study_design_scores_gemma":[0.0003914947,0.00005469812,0.01450315,0.00004633931,0.0003628497,4.250468e-7,0.00006257122,0.3389609,0.6431627,0.002268701,0.000002551117,0.0001836114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598758,0.0000135303,0.03881139,0.0003336145,0.000001503442,0.0006143583,0.0001286008,0.00005973489,0.0001615026],"genre_scores_gemma":[0.8381743,0.00001369467,0.1613644,0.00001170163,0.000006161813,0.0002265705,0.0001651515,0.00001572659,0.00002233856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.339857,"threshold_uncertainty_score":0.6957138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327928930866384,"score_gpt":0.2781680445623553,"score_spread":0.2648887552536914,"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."}}