Distributional shifts of wintering midcontinent greater white‐fronted geese
Bibliographic record
Abstract
Abstract Despite a historically large degree of philopatry to the Gulf Coastal Plains wintering area in the United States and Mexico, the midcontinent population of greater white‐fronted geese (Anser albifrons) has demonstrated changes in their winter distribution in recent decades, warranting investigation into the timing and magnitude of change. We evaluated spatiotemporal patterns in winter band recovery distribution from 1974–2018 and midwinter waterfowl survey counts for midcontinent greater white‐fronted geese. We used an overlap similarity index to compare annual winter band recovery distributions with a historical reference distribution of 1955–1974, followed by a changepoint analysis to assess the timing and magnitude of distributional change. Our analyses revealed a 2‐stage shift in the distribution of winter band recoveries from midcontinent greater white‐fronted geese that occurred following the 1994–1995 season and the 2009–2010 season. As a result, the spatiotemporal distribution of midcontinent greater white‐fronted goose band recoveries can be explained in 3 distinct time eras: the historical era (1974–1995), the transitional era (1995–2010), and the current era (2010–2018). Patterns in midwinter waterfowl survey counts were consistent with changes in winter band recovery distributions, providing further support that midcontinent greater white‐fronted geese have shifted their core winter distribution nearly 750 km northeast over the last 5 decades from the Gulf Coastal Plain to the Mississippi Alluvial Valley. Quantifying the timing and magnitude of this shift in winter distribution of midcontinent greater white‐fronted geese provides clarity to previous patterns in and changes to harvest distribution and could be used to facilitate future decisions regarding harvest management, regulatory frameworks, and habitat conservation planning efforts.
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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 | 0.001 | 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".