{"id":"W2615615043","doi":"10.1021/acs.jproteome.7b00094","title":"Multiplexed MRM-Based Protein Quantitation Using Two Different Stable Isotope-Labeled Peptide Isotopologues for Calibration","year":2017,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University; University of Victoria","funders":"Genome British Columbia; Leading Edge Endowment Fund; Genome Canada","keywords":"Isotopologue; Peptide; Calibration curve; Calibration; Chromatography; Chemistry; Mass spectrometry; Quantitative proteomics; Isotope dilution; Isotope; Computational biology; Biological system; Proteomics; Detection limit; Biology; Biochemistry; Mathematics; Physics; Molecule","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":[0.001382293,0.0001761822,0.0003331887,0.0002189441,0.001129579,0.0003429443,0.0006951942,0.000142486,0.00008766527],"category_scores_gemma":[0.001098135,0.0001478493,0.0001552169,0.00009828414,0.0002103719,0.0005465374,0.0001172038,0.0006463589,0.000002305917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003045422,"about_ca_system_score_gemma":0.0003086703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001659171,"about_ca_topic_score_gemma":0.00003851407,"domain_scores_codex":[0.997948,0.00009047663,0.0006260251,0.0002663384,0.0006060386,0.0004631257],"domain_scores_gemma":[0.9970497,0.0001925381,0.0008986723,0.0006057847,0.001106617,0.0001467164],"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":[0.0004201793,0.0001869644,0.00086354,0.0002457545,0.00002547207,0.000003916381,0.00004540128,0.0002435382,0.9956958,0.001547429,0.00007430161,0.0006476759],"study_design_scores_gemma":[0.001803756,0.0002251834,0.0000639915,0.0003388791,0.00001389843,0.000005977874,0.0000968676,0.04357018,0.9413981,0.01141758,0.0009038879,0.0001617028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8009269,0.00005809686,0.1958724,0.001055736,0.00002414528,0.001873225,0.00004262082,0.00003080537,0.0001160899],"genre_scores_gemma":[0.7453895,0.00001483845,0.2530031,0.00000716931,0.0002582475,0.0007281932,0.00001553342,0.00003885453,0.0005445858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0571307,"threshold_uncertainty_score":0.8687927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1741212022540128,"score_gpt":0.4600102530568096,"score_spread":0.2858890508027968,"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."}}