{"id":"W1972606232","doi":"10.1603/me11210","title":"Passive Surveillance for I. scapularis Ticks: Enhanced Analysis for Early Detection of Emerging Lyme Disease Risk","year":2012,"lang":"en","type":"article","venue":"Journal of Medical Entomology","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de Santé Publique du Québec; Cegep de Saint Hyacinthe; Public Health Agency of Canada","funders":"Public Health Agency of Canada","keywords":"Ixodes scapularis; Tick; Lyme disease; Biology; Population; Logistic regression; Veterinary medicine; Environmental health; Ecology; Ixodidae; Statistics; Virology; Medicine","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":[0.001359908,0.0001565443,0.0006841646,0.0004081237,0.0001088315,0.000005712135,0.0002574368,0.0003329898,0.0004152993],"category_scores_gemma":[0.005028054,0.0001282754,0.0006554454,0.0002990974,0.0002290526,0.0001017363,0.00004349782,0.0002958797,0.000008771926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007215828,"about_ca_system_score_gemma":0.0001672928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001283456,"about_ca_topic_score_gemma":0.00008026924,"domain_scores_codex":[0.9980217,0.0004449326,0.000752723,0.0001728164,0.0001599079,0.0004479738],"domain_scores_gemma":[0.9970614,0.001110754,0.00103932,0.0001847823,0.0003917869,0.0002120042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006912795,0.002540646,0.796628,0.0002868303,0.01932366,0.00003719518,0.001690347,0.0003793714,0.1059676,0.0008100801,0.00116086,0.06426259],"study_design_scores_gemma":[0.007517367,0.001872356,0.9130644,0.00006876919,0.004948986,0.000206176,0.0002475542,0.0002156078,0.06735986,0.001565935,0.002561069,0.0003718901],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8161603,0.003717665,0.177929,0.0003223813,0.001577558,0.0001925965,0.00008005885,0.00001389452,0.000006606067],"genre_scores_gemma":[0.9991679,0.0002285831,0.00009085024,0.00007959651,0.0003070282,0.0000385438,0.00002638699,0.00001619008,0.00004496531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1830076,"threshold_uncertainty_score":0.6019413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009489794620727151,"score_gpt":0.2809541517191002,"score_spread":0.271464357098373,"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."}}