Influence of Weather, Wetland Availability, and Mallard Abundance on Productivity of Great Lakes Mallards (Anas platyrhynchos)
Bibliographic record
Abstract
Waterfowl rely on breeding habitat availability for successful reproduction. Breeding habitat availability likely changes throughout the season and among years as weather patterns change and thus productivity rates are likely susceptible to these changes. We used data from 1961 to 2011 to investigate effects of weather, breeding habitat availability and abundance of breeding mallards ( Anas platyrhynchos ) on productivity rates of mallards breeding in the Great Lake states (Michigan, Minnesota, and Wisconsin; hereafter GLS). We hypothesized that productivity rates would increase with wetter and warmer conditions however, extreme temperatures may have a negative impact and that high breeding density may negatively impact productivity rates. Specifically, we looked at the effects of average June and July temperature and precipitation, the Palmer Hydrological Drought Index (hereafter PHDI), and wetland counts to model productivity rates across the three states for the time series. We used a reduced time series model set to evaluate the impacts of wetland counts on productivity. We found that in general, wetter conditions, as indexed by high positive PHDI values and relationships with pond abundance, positively affected productivity. We believe that breeding habitat availability is likely a reasonable predictor of mallard productivity rates in the GLS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".