{"id":"W2041461813","doi":"10.1021/pr0155174","title":"Peptide End Sequencing by Orthogonal MALDI Tandem Mass Spectrometry","year":2002,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada","funders":"","keywords":"Mass spectrometry; Peptide mass fingerprinting; Tandem mass spectrometry; Bottom-up proteomics; Chemistry; Peptide; Tandem mass tag; Protein mass spectrometry; Proteomics; Mass spectrum; Chromatography; Peptide sequence; Top-down proteomics; Fragmentation (computing); Electrospray; Sample preparation in mass spectrometry; Isobaric labeling; Analytical Chemistry (journal); Quantitative proteomics; Electrospray ionization; Biochemistry; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001961809,0.0001797883,0.0003332914,0.0005174033,0.0002635815,0.0001481552,0.0007404502,0.0001796691,0.04373204],"category_scores_gemma":[0.0003230984,0.0001564705,0.0001994242,0.001038807,0.0001638304,0.0002192335,0.0001099018,0.001977152,0.00009046845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007929811,"about_ca_system_score_gemma":0.0001371419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001549935,"about_ca_topic_score_gemma":0.000001124298,"domain_scores_codex":[0.9966308,0.00009092191,0.0006490707,0.000278792,0.00163796,0.0007124674],"domain_scores_gemma":[0.9982364,0.0002200329,0.0003237931,0.0004124967,0.0004758856,0.0003314467],"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":[0.00001674238,0.00012732,0.002973401,0.00008136547,0.00005563235,0.00008500369,0.00003951283,0.000002113762,0.975729,0.001965899,0.0180339,0.0008900752],"study_design_scores_gemma":[0.0009065793,0.0004634614,0.0004527912,0.0002625935,0.00002771476,0.0007328858,0.0004249647,0.000818707,0.9098189,0.01940726,0.06624532,0.0004388353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7295095,0.002256494,0.004929639,0.004716531,0.00004590075,0.0004438674,0.00006077319,0.0001185968,0.2579187],"genre_scores_gemma":[0.9617547,0.0005224554,0.02501697,0.0000189847,0.0005927546,0.00005265315,0.000004049653,0.00004227289,0.01199516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2459236,"threshold_uncertainty_score":0.9571421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07018039688961876,"score_gpt":0.3422819176072281,"score_spread":0.2721015207176094,"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."}}