{"id":"W4411140207","doi":"10.1016/j.eswa.2025.128482","title":"Leveraging social media and google trends to identify waves of avian influenza outbreaks in USA and Canada","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Food Inspection Agency; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Outbreak; Social media; Influenza A virus subtype H5N1; Computer science; Data science; Virology; World Wide Web; Biology; Virus","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009688881,0.0001070502,0.0002970631,0.0001740949,0.00007261994,0.00001943372,0.00007569359,0.00003658654,0.000009508158],"category_scores_gemma":[0.00003895312,0.00009510553,0.00001238513,0.0003559582,0.00006128252,0.00004352491,0.00004854662,0.00006329764,0.000001811654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062009,"about_ca_system_score_gemma":0.0002430535,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1827205,"about_ca_topic_score_gemma":0.3139726,"domain_scores_codex":[0.9991036,0.00002931405,0.0002879165,0.0002578012,0.0001751221,0.0001462643],"domain_scores_gemma":[0.9993834,0.00009873071,0.00007006303,0.0002539832,0.00006957388,0.0001241939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001544086,0.0001436131,0.9429967,0.0005663317,0.0001682038,0.00001826777,0.007949163,0.00001675208,0.005090912,0.00245944,0.02214729,0.01828887],"study_design_scores_gemma":[0.0008451826,0.00001005816,0.9395419,0.0002068576,0.00001967243,0.000008328809,0.002018371,0.00005414922,0.0001023025,0.00001009671,0.05707135,0.0001117493],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896454,0.002190034,0.0007483184,0.001808168,0.00009477174,0.0009256707,0.0003050729,0.00003491674,0.00424762],"genre_scores_gemma":[0.9984487,0.000009654012,0.0001854399,0.000350859,0.00006214235,0.0004955778,0.00004846359,0.00001008466,0.0003890656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1312521,"threshold_uncertainty_score":0.8227218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766921158636678,"score_gpt":0.3135720727454285,"score_spread":0.2959028611590617,"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."}}