{"id":"W4382278293","doi":"10.2196/43790","title":"Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ixodes scapularis; Tick-borne disease; Tick; Lyme disease; Public health; Disease surveillance; Environmental health; Medicine; Geography; Veterinary medicine; Ixodidae; Virology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008917799,0.0005340391,0.0005560642,0.008536497,0.0005031053,0.002350356,0.001017945,0.0004922825,0.0008106638],"category_scores_gemma":[0.03076681,0.0003994849,0.0007973558,0.00943534,0.0003730982,0.002341117,0.002690346,0.0005095731,0.000140806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220868,"about_ca_system_score_gemma":0.001318613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02772406,"about_ca_topic_score_gemma":0.03261523,"domain_scores_codex":[0.9918282,0.006027878,0.0006317645,0.0007420062,0.0005672597,0.0002029378],"domain_scores_gemma":[0.9785034,0.01358259,0.003963882,0.00161077,0.001992932,0.0003464179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001468428,0.0001992099,0.8433172,0.0007582669,0.0008554948,0.0001732009,0.002078306,0.01649483,0.0006206858,0.003508962,0.00340746,0.1284395],"study_design_scores_gemma":[0.00006920838,0.0004667194,0.6986938,0.001221057,0.001015862,0.0005030964,0.01862614,0.2361251,0.002277631,0.0221406,0.01870634,0.0001544764],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8728814,0.004719344,0.08905224,0.004689284,0.0000649057,0.0007885408,0.02059007,0.00027062,0.006943542],"genre_scores_gemma":[0.9332532,0.001321735,0.06070565,0.000170539,0.00004259968,0.0003972755,0.003921967,0.00001209914,0.0001748693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02772406,"threshold_uncertainty_score":0.05512536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144146286454403,"score_gpt":0.3437045118898983,"score_spread":0.2922630490253542,"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."}}