{"id":"W2995467264","doi":"10.1021/acs.jproteome.9b00434","title":"Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project","year":2019,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council; National Institute of Environmental Health Sciences; Canadian Institutes of Health Research; National Cancer Institute; Canada Research Chairs; National Institutes of Health; Knut och Alice Wallenbergs Stiftelse; Ministry of Health and Welfare; National Institute of General Medical Sciences; Swiss Institute of Bioinformatics; National Institute on Aging","keywords":"Human proteome project; Proteome; Computational biology; Data science; Computer science; Proteomics; Bioinformatics; Biology; 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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004266903,0.0003273211,0.0004789904,0.0004019786,0.001304038,0.0006717948,0.001687911,0.0002392939,0.0001932342],"category_scores_gemma":[0.0003122929,0.0001903741,0.0001979524,0.000978795,0.000443284,0.0004503175,0.0006113424,0.003292103,0.00003940511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284289,"about_ca_system_score_gemma":0.000222489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001428935,"about_ca_topic_score_gemma":0.000004797173,"domain_scores_codex":[0.9957956,0.0003019543,0.0009114195,0.0005338989,0.001706242,0.0007508922],"domain_scores_gemma":[0.9966444,0.0004485387,0.0009349792,0.001088312,0.0007327243,0.0001510939],"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":[0.0001099277,0.0001838621,0.004772615,0.0002343642,0.00009932497,0.00001715194,0.0005850482,0.000001067113,0.9898226,0.001266378,0.0006165133,0.002291151],"study_design_scores_gemma":[0.001650856,0.0008720662,0.008439903,0.001774338,0.00005529409,0.0001167438,0.001134308,0.00007694778,0.9437107,0.01794547,0.02364786,0.000575528],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894039,0.0006164444,0.0002671883,0.003476239,0.00004433576,0.00519311,0.00005218408,0.00006029064,0.0008863101],"genre_scores_gemma":[0.9868198,0.0002491862,0.007695443,0.00006834121,0.0009900209,0.001856771,0.00002323557,0.0001101054,0.002187106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04611192,"threshold_uncertainty_score":0.9999961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1107616288725894,"score_gpt":0.4362435141675213,"score_spread":0.3254818852949319,"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."}}