{"id":"W2979860221","doi":"10.1021/acs.jproteome.9b00542","title":"Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0","year":2019,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Integrative Organismal Systems; U.S. National Library of Medicine; National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; National Institute on Aging; National Eye Institute; Agence Nationale de la Recherche; National Human Genome Research Institute; Division of Biological Infrastructure; National Institute of General Medical Sciences; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Science Foundation; Canadian Institutes of Health Research; Ministry of Health and Welfare; National Cancer Institute","keywords":"Human proteome project; Proteome; Workflow; Pipeline (software); Identifier; Computer science; UniProt; Data science; Computational biology; Proteomics; Bioinformatics; Biology; Database; Genetics","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":[0.003507283,0.0001872211,0.0003702416,0.0005877463,0.0001808987,0.0001536712,0.001940571,0.0001710678,0.0007477216],"category_scores_gemma":[0.0007094052,0.0001549569,0.0001141032,0.0007333018,0.0001128105,0.0006561253,0.0005269474,0.001551406,0.00007983176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002842084,"about_ca_system_score_gemma":0.0003830761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003638714,"about_ca_topic_score_gemma":0.000002053498,"domain_scores_codex":[0.9968172,0.00009635551,0.0009847975,0.0004333406,0.001177126,0.0004911777],"domain_scores_gemma":[0.996391,0.00009911885,0.0005893461,0.001330275,0.00146543,0.0001248429],"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":[0.00009923246,0.00008843682,0.000486455,0.0002641797,0.00004736974,0.00001354064,0.00005236849,0.00001490761,0.9943037,0.0005720361,0.002658249,0.001399493],"study_design_scores_gemma":[0.001439799,0.0008277135,0.0001555142,0.001197922,0.0000322048,0.0002430156,0.0006849752,0.004217788,0.9130154,0.04096503,0.03668703,0.0005335537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8418408,0.000840321,0.1227061,0.004421667,0.0001348818,0.0048862,0.0001743088,0.0002035132,0.02479221],"genre_scores_gemma":[0.4983341,0.0001863514,0.4938816,0.00003133771,0.0009589148,0.0002787235,0.00005406145,0.00008703402,0.006187933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3711755,"threshold_uncertainty_score":0.8187027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1900897560987998,"score_gpt":0.4978729030103682,"score_spread":0.3077831469115684,"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."}}