{"id":"W4391387602","doi":"10.2196/52093","title":"Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Computer science; Data science; Environmental health; Medicine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001010847,0.0002115623,0.0005992249,0.0002404798,0.0001599959,0.0001402248,0.00006861336,0.00007534065,0.00003618636],"category_scores_gemma":[0.0006394893,0.0001955735,0.00003587328,0.0005829435,0.0001387353,0.0002680355,0.00005949741,0.0001811122,0.000004142316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001670773,"about_ca_system_score_gemma":0.002169786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008344758,"about_ca_topic_score_gemma":0.0002216513,"domain_scores_codex":[0.9976233,0.0003019327,0.0006340295,0.0005396619,0.0003169655,0.0005841304],"domain_scores_gemma":[0.9976893,0.000352321,0.0001674112,0.0004009087,0.000241305,0.001148719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009222546,0.0004778212,0.4392726,0.01137118,0.0002260702,0.000377515,0.001547221,0.00002724448,0.000697783,0.002672977,0.08246567,0.4599417],"study_design_scores_gemma":[0.002118235,0.002066591,0.7767285,0.0007193201,0.00001001008,0.0006716911,0.0008140381,0.01357216,0.0000115382,0.0001110765,0.202552,0.0006248198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414241,0.02851886,0.006501241,0.01264288,0.000524045,0.002409848,0.007509987,0.0003481256,0.0001208987],"genre_scores_gemma":[0.9871022,0.004720536,0.004118695,0.00198294,0.0001342402,0.00003304495,0.0016451,0.00003999048,0.0002232965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4593169,"threshold_uncertainty_score":0.7975256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030747116808941,"score_gpt":0.3721334794671409,"score_spread":0.2690587677862467,"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."}}