Winter weather and waterfowl surveys in north‐western Ontario, Canada
Bibliographic record
Abstract
Abstract Aim An analysis is presented to examine whether variation in breeding waterfowl estimates can be explained by weather patterns prior to annual surveys. Location The location of the study is north‐western Ontario, Canada. Methods Annual, systematic survey data for breeding waterfowl are available from the 1950s to the present for north‐western Ontario. Regional monthly climate data for this area were compiled using weather data derived from interpolated annual climate surfaces. These data were analysed using stepwise multiple linear regression for each species and for waterfowl functional groups to assess whether monthly climate data accounted for some of the variation in waterfowl numbers. Results For all dabbling ducks pooled, 12% of the variation in annual abundance was explained by April temperatures, with more dabbling ducks observed in years when April was relatively cool. For diving ducks, 23% of the variation in pooled abundance was explained by April temperatures and February precipitation, where more diving ducks were observed in years when February had relatively less precipitation and April was cool. Patterns for individual species varied. Main conclusions Mean monthly weather data for months prior to surveys explained some of the variation in numbers of waterfowl observed in annual surveys. This suggests that future incorporation of weather data into waterfowl population models may help refine population estimates.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".