{"id":"W2956312129","doi":"10.1186/s12014-019-9250-4","title":"Correction to: A proteome-wide immuno-mass spectrometric identification of serum autoantibodies","year":2019,"lang":"en","type":"erratum","venue":"Clinical Proteomics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University Health Network; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Proteome; Identification (biology); Proteomics; Autoantibody; Computational biology; Data science; Computer science; Medicine; Bioinformatics; Immunology; Chemistry; Biology; Antibody; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003177109,0.002512327,0.001850377,0.003883552,0.002787289,0.0032881,0.002869842,0.00590434,0.04168669],"category_scores_gemma":[0.03936733,0.001069953,0.001749295,0.002079246,0.001957366,0.001721578,0.001764796,0.01003837,0.03588112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003193389,"about_ca_system_score_gemma":0.004092697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00947341,"about_ca_topic_score_gemma":0.00922815,"domain_scores_codex":[0.9954485,0.0005676101,0.0009020467,0.0007683727,0.001931731,0.0003815678],"domain_scores_gemma":[0.9766828,0.005996833,0.00144717,0.001473547,0.013013,0.001386567],"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":[0.00008143377,0.0000167501,0.0001216747,0.0001504792,0.00002459235,0.0007884631,0.00003290783,0.00005105972,0.0001900581,0.000670635,0.9894271,0.008444816],"study_design_scores_gemma":[0.00005888155,0.00004792142,0.00094495,0.0002823044,0.00006823321,0.002807576,0.00005571763,0.0003720623,0.0008761006,0.001258737,0.9931768,0.00005075055],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003055382,0.0009473216,0.001372248,0.04305265,0.9508826,0.00002377455,0.001123478,0.0004482858,0.001844082],"genre_scores_gemma":[0.02658567,0.01156884,0.01526455,0.1416814,0.5278113,0.0002530051,0.006582909,0.003192666,0.2670595],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04168669,"threshold_uncertainty_score":0.1394559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04274434254344835,"score_gpt":0.3845542818393967,"score_spread":0.3418099392959483,"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."}}