{"id":"W4234184340","doi":"10.1255/ejms.400","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 Mass Spectrometry","year":2001,"lang":"en","type":"article","venue":"European Journal of Mass Spectrometry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trypsin; Chemistry; Digestion (alchemy); Chromatography; Peptide; Aminopeptidase; Bottom-up proteomics; Mass spectrometry; Leucine; Matrix-assisted laser desorption/ionization; Peptide sequence; Sample preparation; Amino acid; Sample preparation in mass spectrometry; Peptide mass fingerprinting; Biochemistry; Protein mass spectrometry; Enzyme; Desorption; Electrospray ionization; Proteomics; 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":[0.0009808756,0.0001940117,0.0002974034,0.0002002174,0.0001436459,0.00004885048,0.0003020459,0.00007495384,0.00005601927],"category_scores_gemma":[0.0002076541,0.0001642802,0.0001442456,0.0007705116,0.0001865986,0.0002578098,0.0000496721,0.0002826202,5.851932e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002309712,"about_ca_system_score_gemma":0.00006620599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004301345,"about_ca_topic_score_gemma":4.796385e-7,"domain_scores_codex":[0.9978949,0.0001772984,0.001128358,0.0002730508,0.0003452339,0.0001811175],"domain_scores_gemma":[0.9970179,0.0000495809,0.002057787,0.0003927299,0.0003956241,0.00008640868],"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":[0.0002251052,0.00009233869,0.00315455,0.0002771939,0.000030387,0.000001151768,0.0000279372,0.0001002119,0.9951171,0.0003359416,0.00002715866,0.0006109324],"study_design_scores_gemma":[0.001223992,0.0001559768,0.003950635,0.0002241126,0.00007657932,0.00005732185,0.00006214406,0.0006961206,0.9900146,0.003241366,0.000146075,0.0001510723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6077721,0.0000202151,0.3873178,0.000117815,0.00001153296,0.004612715,0.00004139841,0.00002024628,0.00008613491],"genre_scores_gemma":[0.8570908,0.00000982965,0.1419133,0.00000589909,0.0001230897,0.0006528471,0.00002492671,0.00004131836,0.00013802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2493187,"threshold_uncertainty_score":0.669915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503231619572674,"score_gpt":0.2848960679883181,"score_spread":0.2598637517925914,"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."}}