Time series analysis of <i>Varroa destructor</i> counts in Ontario honey bee colonies and their association with weather variables
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
The parasitic mite, Varroa destructor, is widely considered the most important risk factor for honey bee colony health in Canada, consistently associated with colony loss and poor colony health. Examining the temporal epidemiology of Varroa mites is crucial for identifying population-level trends, understanding seasonal patterns, and evaluating potential associations with external risk factors. This study examines the temporal patterns of observed Varroa in Ontario, Canada, over a five-year period (2015–2019), using provincial ministry inspection data. Through time-series decomposition and regression modelling, seasonal patterns and long-term trends in mite counts were described, with tests for associations with historical weather data, both instantaneous and lagged, to account for delayed effects on mite counts. A repetitive seasonal pattern and a slight decreasing trend were observed in mite counts throughout the duration of the study. Associations with ambient temperature and dew point temperature were observed when a seven-week lag was applied. These results provide an epidemiological perspective on Varroa mite infestations over time, offering valuable insights for surveillance by establishing a reference for expected mite levels.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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