{"id":"W2027961368","doi":"10.1021/ac010583a","title":"Fractionation of Isotopically Labeled Peptides in Quantitative Proteomics","year":2001,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Canadian Institute for Theoretical Astrophysics; Purdue University","keywords":"Chemistry; Derivatization; Peptide; Chromatography; Resolution (logic); Deuterium; Fractionation; Isobaric labeling; Quantitative proteomics; Isotope; Reversed-phase chromatography; Stable isotope ratio; Reagent; Proteomics; High-performance liquid chromatography; Mass spectrometry; Tandem mass spectrometry; Biochemistry; Organic chemistry; Protein mass spectrometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006642491,0.0001029634,0.0001718515,0.00002186789,0.00002798929,0.000008598925,0.0001450621,0.0001387098,0.0005762486],"category_scores_gemma":[0.0001932554,0.0001073096,0.00006113462,0.0002030924,0.00008573045,0.00006928114,0.0000354782,0.0002397631,0.00000715338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007776094,"about_ca_system_score_gemma":0.00003548017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002523065,"about_ca_topic_score_gemma":0.000004889084,"domain_scores_codex":[0.9991732,0.000003900133,0.0003231083,0.0002169669,0.0001332571,0.0001495832],"domain_scores_gemma":[0.9994167,0.00009289127,0.0001152435,0.0002223395,0.0001015252,0.00005127328],"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":[0.00007624306,0.0002466363,0.01967252,0.0001038474,0.00002050363,0.000006364499,0.00003695951,0.0001188764,0.9666263,0.01251247,0.00007872294,0.0005005943],"study_design_scores_gemma":[0.0003601788,0.00001183639,0.0005002253,0.00006184902,0.00001415598,0.00000872836,0.0001321475,0.009431042,0.9691327,0.0172384,0.002928421,0.0001803502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463555,0.00002211057,0.01440792,0.0004684347,0.000002582691,0.00008991406,0.00001153297,0.00005271221,0.0385893],"genre_scores_gemma":[0.9619794,0.00007189649,0.03589493,0.00003378212,0.00003084242,0.000056438,0.00003474152,0.00001249662,0.001885477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03670382,"threshold_uncertainty_score":0.6309518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0206158822459869,"score_gpt":0.3154206308603127,"score_spread":0.2948047486143258,"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."}}