Intercolony variation in growth of black brant goslings on the Yukon‐Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Abstract Recent declines in black brant (Branta bernicla nigricans) are likely the result of low recruitment. In geese, recruitment is strongly affected by habitat conditions experienced by broods because gosling growth rates are indicative of forage conditions during brood rearing and strongly influence future survival and productivity. In 2006–2008, we studied gosling growth at 3 of the 4 major colonies on the Yukon‐Kuskokwim Delta, Alaska. Estimates of age‐adjusted gosling mass at the 2 southern colonies (approx. 30% of the world population of breeding black brant) was low (gosling mass at 30.5 days ranged 346.7 ± 42.5 g to 627.1 ± 15.9 g) in comparison to a third colony (gosling mass at 30.5 days ranged 640.0 ± 8.3 g to 821.6 ± 13.6 g) and to most previous estimates of age‐adjusted mass of brant goslings. Thus, our results are consistent with the hypothesis that poor gosling growth is negatively influencing the brant population. There are 2 non‐mutually exclusive explanations for the apparent growth rates we observed. First, the population decline may have been caused by density‐independent factors and habitat capacity has declined along with the population as a consequence of the unique foraging feedback between brant and their grazing habitats. Alternatively, a reduction in habitat capacity, as a result of changes to the grazing system, may have negatively influenced gosling growth, which is contributing to the overall long‐term population decline. We found support for both explanations. For colonies over habitat capacity we recommend management to enhance foraging habitat, whereas for colonies below habitat capacity we recommend management to increase nesting productivity. © 2010 The Wildlife Society.
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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 | 0.000 |
| 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".