{"id":"W2062022660","doi":"10.1038/npre.2009.3552.1","title":"VO: Vaccine Ontology","year":2009,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Computer science; Information retrieval","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"],"consensus_categories":[],"category_scores_codex":[0.0002692768,0.0004467314,0.0004343475,0.00009973328,0.00007590406,0.00008592261,0.0007305582,0.002504681,0.00003513253],"category_scores_gemma":[0.0001878515,0.0004037448,0.0002568488,0.00007848059,0.00001891597,0.000004945149,0.0009319452,0.001691599,0.00001569249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003168138,"about_ca_system_score_gemma":0.0001093284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001262434,"about_ca_topic_score_gemma":0.00002141789,"domain_scores_codex":[0.9983697,0.00003167852,0.0004278842,0.0005840522,0.0001783979,0.0004082646],"domain_scores_gemma":[0.9986311,0.000009757693,0.0003068474,0.0007635797,0.0001904375,0.00009832942],"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":[0.001288613,0.0005819121,0.004858213,0.001387775,0.001605879,0.00001689335,0.0009675437,0.0006803435,0.3316923,0.005620568,0.5752326,0.07606738],"study_design_scores_gemma":[0.003379442,0.001359979,0.01924341,0.0004631373,0.0004157606,0.0002932295,0.0001895682,0.001728321,0.2604108,0.01229245,0.6972499,0.002973971],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.911287,0.02927034,0.001926713,0.004029181,0.003159872,0.001254709,0.0000928546,0.0001630011,0.04881637],"genre_scores_gemma":[0.9855446,0.001605646,0.007242568,0.00176323,0.001199139,0.00004682247,0.001248099,0.0000551402,0.00129481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1220173,"threshold_uncertainty_score":0.9998415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007308204226152532,"score_gpt":0.2574759122534573,"score_spread":0.2501677080273048,"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."}}