The Effects of Weather and Environmental Factors on West Nile Virus Mosquito Abundance in Greater Toronto Area
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
Abstract We investigated how weather conditions and environmental factors affect the spatiotemporal variability in Culex pipiens population using the data collected from a surveillance program in Ontario, Canada, from 2005 to 2008. This study assessed the relative influences of temperature and precipitation on the temporal patterns of mosquito abundance using harmonic analysis and examined the associations with major landscape predictors, including land-use type, population density, and elevation, on the spatial patterns of mosquito abundance. The intensity of trapping efforts on the mosquito abundance at each trap site was examined by comparing the spatial distribution of mosquito abundance in relation to the spatial intensity of trapping efforts. The authors used a mixed effects modeling approach to account for potential dependent structure in mosquito surveillance data due to repeated observations at single trap sites and/or similar mosquito abundance at nearby trap sites each week. The model fit was improved by taking into account the nested structure of mosquito surveillance data and incorporating the temporal correlation in random effects.
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) | 0.000 | 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