{"id":"W2026103657","doi":"10.1016/s1044-0305(99)00126-9","title":"Discerning matrix-cluster peaks in matrix-assisted laser desorption/ionization time-of-flight mass spectra of dilute peptide mixtures","year":2000,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Chemistry; Mass spectrometry; Cluster (spacecraft); Analyte; Matrix (chemical analysis); Matrix-assisted laser desorption/ionization; Peptide; Ionization; Mass spectrum; Analytical Chemistry (journal); Ion; Time-of-flight mass spectrometry; Ion suppression in liquid chromatography–mass spectrometry; Desorption; Chromatography; Tandem mass spectrometry; Physical chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007165898,0.0006857021,0.0003817693,0.001429645,0.0005252588,0.0005364502,0.0003910821,0.0009106367,0.0008470244],"category_scores_gemma":[0.001096251,0.0002794719,0.0002001142,0.0007513733,0.0004726949,0.0008103402,0.000502496,0.001083514,0.0006850486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002611024,"about_ca_system_score_gemma":0.0004108791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004179882,"about_ca_topic_score_gemma":0.0007424,"domain_scores_codex":[0.9995018,0.00008204202,0.00002329466,0.000116588,0.0002311409,0.00004499851],"domain_scores_gemma":[0.999231,0.0003169733,0.0001335769,0.00003814077,0.000161553,0.0001188843],"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":[0.00005335454,0.00001093851,0.0001569688,0.00002467989,0.000003776945,0.00004472165,0.00002252725,0.00003374496,0.9978528,0.00006172799,0.00002140193,0.001713251],"study_design_scores_gemma":[0.00001514604,0.0002079411,0.0060626,0.000009404033,0.00001824973,0.0006492569,0.00004953539,0.001997642,0.9893338,0.000306416,0.00133342,0.00001662773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8197458,0.007065769,0.1673716,0.0003150095,0.0001395485,0.0002983289,0.000654505,0.0009408227,0.003468652],"genre_scores_gemma":[0.7323159,0.004927106,0.2570195,0.0004559921,0.00008204622,0.0003490755,0.001379102,0.0002188475,0.003252425],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001429645,"threshold_uncertainty_score":0.003789783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007545066539459979,"score_gpt":0.2637293409624486,"score_spread":0.2561842744229886,"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."}}