{"id":"W2902065303","doi":"10.2903/sp.efsa.2018.en-1498","title":"INNUENDO: A cross‐sectoral platform for the integration of genomics in the surveillance of food‐borne pathogens","year":2018,"lang":"en","type":"article","venue":"EFSA Supporting Publications","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Eusko Jaurlaritza; China Scholarship Council; European Society of Clinical Microbiology and Infectious Diseases; European Food Safety Authority; Euskal Herriko Unibertsitatea; Public Health Agency; Public Health Agency of Canada","keywords":"Workflow; Annotation; Computer science; Software; World Wide Web; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001543376,0.00007497009,0.0001469009,0.00001660109,0.0001629573,0.00003114445,0.0004230504,0.00006552528,0.00006367844],"category_scores_gemma":[0.00115106,0.00002410189,0.00009025547,0.0004358779,0.0001937236,0.00008115124,0.00003706946,0.00007508939,0.000001916476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000148539,"about_ca_system_score_gemma":0.00002473281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002277564,"about_ca_topic_score_gemma":0.003804704,"domain_scores_codex":[0.9989721,0.00007545182,0.0005050831,0.0001592571,0.00008407499,0.0002039934],"domain_scores_gemma":[0.9978203,0.001328645,0.000428065,0.0001380107,0.0002639098,0.00002109083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005985676,0.0001796895,0.7066723,0.0000123044,0.00003553654,8.603028e-8,0.00240907,0.00001287552,0.2058588,0.02942421,0.003091007,0.05224421],"study_design_scores_gemma":[0.00008516954,0.0002268301,0.9827181,0.000003330851,0.000004866769,0.000003223819,0.0006521377,0.0004027833,0.002467022,0.00211559,0.01126521,0.00005576814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941776,0.00005508154,0.0003151414,0.004460515,0.00008259378,0.0003693563,0.0002058246,0.00001505506,0.0003188475],"genre_scores_gemma":[0.9990644,0.000009259687,0.0002246053,0.0002371601,0.0001659036,0.00009042188,0.00016589,9.696311e-7,0.00004133564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2760457,"threshold_uncertainty_score":0.2123114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08473344662703562,"score_gpt":0.3234596302332756,"score_spread":0.23872618360624,"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."}}