Investigating the Influence of Climate Change on West Nile Virus Occurrence in Ontario, Canada
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
West Nile virus (WNV) first appeared in North America during the spring season of 2002, growing to be the most common mosquito borne disease in Ontario, and becoming the primary cause of viral encephalitis. Climate change is hypothesized to increase Ontarian contact with WNV as climatic variables have been studied to amplify bridge vector replication in infected mosquitos. To further investigate this relation, we developed 2 models—Logistic Regression and Decision Tree—to predict WNV occurrence in mosquitos by these factors in Ontario. The Decision Tree model reflected a higher overall accuracy of 100% in training and 93% in testing. Both the Decision Tree model and Logistic Regression model found positive correlations between factors of night temperature, night humidity, NDVI, and rainfall, to the occurrence of mosquito WNV output. While the Logistic Regression model itself, found the occurrence of mosquito WNV cases to have a negative correlation to the factors: wind speed, day humidity, and total precipitation. Our findings can provide useful insight on when to warn the public to take special measures against WNV disease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it