{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05094666,0.001966779,0.002035898,0.009242536,0.002543607,0.006669108,0.007513809,0.003896717,0.02459125],"category_scores_gemma":[0.07829881,0.00208145,0.002510827,0.007024693,0.001823068,0.004074226,0.004951443,0.006835845,0.03296309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002733339,"about_ca_system_score_gemma":0.01441875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00675388,"about_ca_topic_score_gemma":0.005215881,"domain_scores_codex":[0.9728568,0.01094385,0.006494927,0.001682893,0.007034337,0.0009872044],"domain_scores_gemma":[0.9217656,0.01944814,0.004977714,0.009003852,0.0427576,0.002047158],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006768472,0.0000937971,0.001978148,0.003870273,0.000112182,0.0005094921,0.0007734676,0.0005045975,0.00882974,0.009716123,0.9112291,0.06170634],"study_design_scores_gemma":[0.0001283792,0.00006563227,0.002886167,0.002024361,0.00006174125,0.0005179757,0.0002632343,0.000798998,0.005193109,0.008630635,0.9793386,0.00009107812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009273075,0.0135164,0.4220356,0.03597245,0.008757575,0.01211982,0.3354299,0.07184803,0.09104713],"genre_scores_gemma":[0.01239013,0.008025422,0.5526695,0.02041425,0.001385006,0.01216076,0.3589155,0.01538642,0.01865309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9490533,"threshold_uncertainty_score":0.2694349,"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."}}