{"id":"W1967438882","doi":"10.1002/1615-9861(200108)1:8<987::aid-prot987>3.0.co;2-5","title":"Investigation of the applicability of a sequential digestion protocol using trypsin and leucine aminopeptidase M for protein identification by matrix-assisted laser desorption/ionization - time of flight mass spectrometry","year":2001,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Trypsin; Digestion (alchemy); Chemistry; Chromatography; Bottom-up proteomics; Peptide; Mass spectrometry; Aminopeptidase; Peptide sequence; Leucine; Sample preparation; Amino acid; Sample preparation in mass spectrometry; Matrix-assisted laser desorption/ionization; Peptide mass fingerprinting; Protein mass spectrometry; Biochemistry; Enzyme; Electrospray ionization; Desorption; Proteomics; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00146059,0.0008384267,0.0004887445,0.0003611536,0.0002975749,0.0005423426,0.00059653,0.0008055584,0.0004898093],"category_scores_gemma":[0.001253049,0.0002855985,0.0003164586,0.0003392589,0.0003255904,0.0004409729,0.0003692565,0.0005521073,0.0006821245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002534568,"about_ca_system_score_gemma":0.0003540709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003669193,"about_ca_topic_score_gemma":0.0004912681,"domain_scores_codex":[0.9991037,0.000181435,0.00007165216,0.0001914526,0.0003677788,0.00008382381],"domain_scores_gemma":[0.9993634,0.0002521246,0.0001177367,0.00005530678,0.0001635336,0.00004797293],"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":[0.00001593617,0.00001655132,0.00008145291,0.00004240274,0.00000294202,0.00004063821,0.000007972742,0.00004684402,0.9983553,0.0000125997,0.000009578146,0.00136766],"study_design_scores_gemma":[0.000007132654,0.000529178,0.001722737,0.00001127176,0.00002506808,0.0006062837,0.00003464767,0.001958471,0.993016,0.00003934327,0.002038973,0.00001085981],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8752803,0.006652887,0.1148544,0.0002758856,0.0001152539,0.0005626343,0.00022087,0.000226186,0.001811435],"genre_scores_gemma":[0.7739628,0.008574072,0.2127354,0.0002358831,0.00005356636,0.000537982,0.0009076776,0.0001062791,0.002886401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00146059,"threshold_uncertainty_score":0.007724464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016091184670016,"score_gpt":0.2859629364183077,"score_spread":0.2658020245716076,"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."}}