Characterizing areas of potential human exposure to eastern equine encephalitis virus using serological and clinical data from horses
Bibliographic record
Abstract
Eastern equine encephalitis (EEE) is a rare but severe emerging vector-borne disease affecting human and animal populations in the northeastern United States where it is endemic. Key knowledge gaps remain about the epidemiology of EEE virus (EEEV) in areas where its emergence has more recently been reported. In Eastern Canada, viral activity has been recorded in mosquitoes and horses throughout the 2000s but cases of EEEV in humans have not been reported so far. This study was designed to provide an assessment of possible EEEV human exposure by modelling environmental risk factors for EEEV in horses, identifying high-risk environments and mapping risk in the province of Quebec, Canada. According to logistic models, being located near wooded swamps was a risk factor for seropositivity or disease in horses [odds ratio (OR) 4·15, 95% confidence interval (CI) 1·16-14·8) whereas being located on agricultural lands was identified as protective (OR 0·75, 95% CI 0·62-0·92). A better understanding of the environmental risk of exposure to EEEV in Canada provides veterinary and public health officials with enhanced means to more effectively monitor the emergence of this public health risk and design targeted surveillance and preventive measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".