{"id":"W2194477621","doi":"10.1038/nmeth.3655","title":"MSPLIT-DIA: sensitive peptide identification for data-independent acquisition","year":2015,"lang":"en","type":"letter","venue":"Nature Methods","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Spinal Cord Injury BC; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Identification (biology); Computational biology; Peptide; Chemistry; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001684996,0.0004504395,0.0005191411,0.0001167789,0.0001761943,0.0001157228,0.001048615,0.003602503,0.00006330606],"category_scores_gemma":[0.0005064196,0.0004593667,0.0001860422,0.0001624013,0.00007338611,0.0002110892,0.0003496638,0.004238829,0.0000215603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003809044,"about_ca_system_score_gemma":0.0001516432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153518,"about_ca_topic_score_gemma":0.000002741645,"domain_scores_codex":[0.9971549,0.0001506682,0.0005546386,0.001275757,0.0004504996,0.0004135624],"domain_scores_gemma":[0.9954128,0.0006166254,0.0006747323,0.002513679,0.0006995747,0.0000826016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002578394,0.00002093601,8.046727e-7,0.0002613636,0.00007912942,0.0000120022,0.00002730558,0.000001909932,0.07674491,0.0005906643,0.9076573,0.01457789],"study_design_scores_gemma":[0.0001892267,0.000008197032,0.000001542409,0.0000629967,0.0001864927,0.00002909586,0.00001919406,0.0004960125,0.1858291,0.02699484,0.7857871,0.0003962],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001711677,0.0006926027,0.8969375,0.09141976,0.0003821192,0.0009588728,0.003684199,0.0003869387,0.005520906],"genre_scores_gemma":[0.0001055789,0.0000701336,0.8752729,0.07149022,0.005551902,0.0008742272,0.03701947,0.0001493219,0.00946625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1218702,"threshold_uncertainty_score":0.9997858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05070079411617558,"score_gpt":0.4229341241777745,"score_spread":0.3722333300615989,"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."}}