{"id":"W2052913376","doi":"10.1021/ac802106z","title":"Off-Line Two-Dimensional Liquid Chromatography with Maximized Sample Loading to Reversed-Phase Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry for Shotgun Proteome Analysis","year":2009,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Chemistry; Chromatography; Mass spectrometry; Tandem mass spectrometry; Proteome; Shotgun proteomics; Bottom-up proteomics; Shotgun; Peptide; Protein mass spectrometry; Liquid chromatography–mass spectrometry; Electrospray ionization; Elution; Sample preparation; Selected reaction monitoring; Proteomics; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001154448,0.001381779,0.0009579386,0.000780437,0.0004383969,0.001395764,0.001247817,0.0007602235,0.00101794],"category_scores_gemma":[0.0009766723,0.0005186551,0.0006736183,0.0006525742,0.0005162095,0.0008137632,0.0009243534,0.001403104,0.001483378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005152308,"about_ca_system_score_gemma":0.0009635061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005723818,"about_ca_topic_score_gemma":0.001366535,"domain_scores_codex":[0.9985165,0.0002962055,0.00008977656,0.000199879,0.000815779,0.00008187888],"domain_scores_gemma":[0.9995204,0.0001455056,0.00008650646,0.00007349309,0.0001351946,0.00003884688],"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":[0.00004711576,0.00007424603,0.0001906999,0.00007984542,0.000011435,0.00004742926,0.00001929078,0.0001579786,0.9894315,0.0001818949,0.0002180003,0.00954052],"study_design_scores_gemma":[0.00002001482,0.0001626656,0.0009152908,0.00001228652,0.00002235077,0.0003245557,0.00001109095,0.006059783,0.9882829,0.0001771429,0.003988116,0.00002372881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2889708,0.004767451,0.6943227,0.0006796515,0.0003054515,0.001102531,0.001342818,0.004618283,0.003890368],"genre_scores_gemma":[0.1950969,0.004133271,0.791465,0.0008124984,0.000145146,0.001173251,0.003295601,0.0006270043,0.00325132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001395764,"threshold_uncertainty_score":0.006105423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187100134300097,"score_gpt":0.2855378332127623,"score_spread":0.2736668318697613,"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."}}