{"id":"W2568086282","doi":"10.1016/j.jprot.2016.12.005","title":"Corrigendum to “Introduction to the HUPO 2015 Special Issue” [J. Proteomics (2016) 1–2]","year":2017,"lang":"en","type":"erratum","venue":"Journal of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute for Research in Immunology and Cancer; Genome British Columbia; Université de Montréal; University of Victoria","funders":"","keywords":"Library science; Data science; Computational biology; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001024141,0.0006982895,0.001031153,0.0002874273,0.0007371707,0.0004427387,0.002977674,0.000934407,0.001055769],"category_scores_gemma":[0.0008591865,0.0005463858,0.0005047281,0.0002177885,0.0001325165,0.0002921007,0.0005578265,0.003565728,0.0004154035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008636382,"about_ca_system_score_gemma":0.00112045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003745587,"about_ca_topic_score_gemma":0.00004446357,"domain_scores_codex":[0.9962321,0.00004653982,0.001444548,0.0007174265,0.0008486824,0.0007107245],"domain_scores_gemma":[0.9930939,0.00002153849,0.002997215,0.002236035,0.001140264,0.0005110321],"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":[0.0002717917,0.00009393846,0.000004320018,0.000106805,0.00009500729,0.00001653806,0.0001993612,0.00009444401,0.0238485,0.0001945977,0.9707823,0.004292361],"study_design_scores_gemma":[0.0002963505,0.000210054,0.000005847648,0.0004081061,0.0001290288,0.0002223271,0.00007247092,0.00003344172,0.05829291,0.002362727,0.9373834,0.0005833461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001202498,0.001410643,0.4808156,0.2215712,0.1711249,0.01389152,0.001851021,0.0004251825,0.1077074],"genre_scores_gemma":[0.00001722091,0.0008980366,0.2643052,0.0004857611,0.2438295,0.0005521861,0.00009417019,0.0002100953,0.4896078],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3819004,"threshold_uncertainty_score":0.9998574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851755457194884,"score_gpt":0.3016159881879553,"score_spread":0.2830984336160065,"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."}}