Long‐term trends in weather severity indices for dabbling ducks in eastern North America
Bibliographic record
Abstract
ABSTRACT Annual distributions of waterfowl during the nonbreeding period can influence ecological, cultural, and economic relationships. We used previously developed Weather Severity Indices (WSI) that explained migration by dabbling ducks in eastern North America and weather data from the North American Regional Reanalysis to develop an open‐access internet‐based tool (i.e., WSI web app) to visualize and query WSI data. We used data generated by the WSI web app to determine whether the weather known to elicit southerly migration by dabbling ducks had changed, from October to April 1979 to 2013. We detected that the amount of area in the Mississippi and Atlantic Flyways with weather severe enough to cause southerly migration decreased during 1) October–December for American wigeon ( Mareca americana ), green‐winged teal ( Anas crecca ), and northern shoveler ( Spatula clypeata ); 2) December–January for mallard ( A . platyrhynchos ), American black duck ( A . rubripes ), and northern pintail ( A . acuta ); and 3) February–April for mallard, American black duck, gadwall ( M . strepera ), American wigeon, green‐winged teal, and northern shoveler. Results were consistent with prior research indicating that weather causing autumn and winter migration of dabbling ducks has become increasingly mild in the past 3 decades. The WSI web app enables users to query daily data and maps by species and by Flyway, Joint Venture, Landscape Conservation Cooperative, and State. We encourage those with corresponding data on participation and satisfaction by waterfowl enthusiasts (i.e., birders and hunters) to test for relationships with the WSI because of the implications for conservation funding, especially if autumn and winter weather severity continues to become increasingly mild as predicted. © 2017 The Wildlife Society.
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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 | 0.000 | 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) | 0.020 | 0.001 |
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".