Evaluation of a West Nile virus risk-assessment tool used at a local health unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Ontario, public health units collect surveillance data on vector-borne diseases (VBD) to determine emerging trends and develop VBD management strategies. Risk-assessment tools that are simple and easily applied can provide public health practitioners with objective evaluations of the risk of West Nile virus (WNV) activity in their jurisdiction. This study was conducted to evaluate an existing WNV risk-assessment tool used by a public health unit in southern Ontario. The purpose of this study was to: (i) describe the trends for WNV in mosquito and human cases in the Region of Peel, Ontario, Canada, and (ii) investigate the ability of the risk-assessment tool to predict positive human cases and positive mosquito traps in the following weeks. Data were collected from 2011 to 2016 and analysed using simple descriptive statistics and Fisher’s exact tests. This study found the tool includes variables that are not significant in predicting WNV activity in the following weeks. The current tool should be revised to remove variables that are not significant in predicting risk and add additional variables that have been shown to be effective predictors in other studies, such as rainfall and human WNV cases in the previous year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it