{"id":"W2058457224","doi":"10.1016/s1570-0232(02)00565-2","title":"Low-mass proteome analysis based on liquid chromatography fractionation, nanoliter protein concentration/digestion, and microspot matrix-assisted laser desorption ionization mass spectrometry","year":2002,"lang":"en","type":"article","venue":"Journal of Chromatography B","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Chemistry; Chromatography; Mass spectrometry; Capillary electrophoresis–mass spectrometry; High-performance liquid chromatography; Fractionation; Sample preparation in mass spectrometry; Proteome; Bottom-up proteomics; Protein mass spectrometry; Sample preparation; Fraction (chemistry); Resolution (logic); Capillary electrophoresis; Matrix-assisted laser desorption/ionization; Analytical Chemistry (journal); Tandem mass spectrometry; Electrospray ionization; Desorption; Biochemistry; Adsorption","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":[0.0005149065,0.0006851092,0.001050561,0.0006771929,0.0006613485,0.0007208916,0.000582687,0.0004531643,0.0007013231],"category_scores_gemma":[0.0005044297,0.0002487829,0.0003684337,0.0004414989,0.0004701252,0.0006622896,0.0003644193,0.001138434,0.0009131543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006173248,"about_ca_system_score_gemma":0.0005762269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009929863,"about_ca_topic_score_gemma":0.002328779,"domain_scores_codex":[0.999631,0.00006291798,0.00003269482,0.00007852061,0.000154419,0.00004039151],"domain_scores_gemma":[0.9997062,0.00008593227,0.00003494607,0.000050795,0.00007697169,0.00004511024],"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":[0.00006735531,0.00003615462,0.0000970357,0.00003796962,0.000005110304,0.000009752856,0.000006776901,0.00003337063,0.996841,0.0001092754,0.0000946781,0.002661546],"study_design_scores_gemma":[0.000025072,0.0001349535,0.004657572,0.000008182852,0.00002198022,0.0002137935,0.00001602751,0.004214325,0.9873254,0.0004880609,0.002878383,0.00001619054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5379287,0.008164674,0.4411941,0.001428994,0.0003266583,0.0006166821,0.004007333,0.002319687,0.004013141],"genre_scores_gemma":[0.5652757,0.006330246,0.4089422,0.001255329,0.0002016195,0.0009384879,0.008056183,0.0005565475,0.008443769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001050561,"threshold_uncertainty_score":0.004479051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006485488257802094,"score_gpt":0.2287876775721468,"score_spread":0.2223021893143447,"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."}}