SURVIVAL OF YOUNG GREATER SNOW GEESE (CHEN CAERULESCENS ATLANTICA) DURING FALL MIGRATION
Bibliographic record
Abstract
The many hazards that await birds along their migratory routes may negatively affect their survival, especially among newly fledged young. We estimated survival of young Greater Snow Geese (Chen caerulescens atlantica) during fall migration from the High Arctic to temperate areas and examined factors affecting their survival over a five-year period, using two approaches. First, each year (1993–1997), we banded fledglings and adults in mid-August, just before their departure from Bylot Island in the High Arctic (Nunavut, Canada), and again at an important staging area 3,000 km to the south at the Cap Tourmente National Wildlife Area (Québec, Canada) in October; recovery data from those two banding periods allowed estimation of survival during fall migration. Second, we visually determined brood size of neck-banded females before and after the main portion of the migratory flight, to estimate survival of young. The two approaches yielded similar survival estimates and showed the same interannual variation, thus suggesting that estimates were reliable. Mortality of young shortly after fledging and during the fall migration was high, compared with that of adults (monthly survival 0.662 in young vs. 0.989 in adults). However, mortality of young after migration was similar to that of adults (monthly survival 0.969 in young vs. 0.972 in adults). Migration survival of young varied considerably among annual cohorts (range of 0.119–0.707 over five years), and most of the mortality appeared to be natural. Survival was especially low in years when (1) temperatures at time of fledging and start of migration were low (i.e. near or below freezing), (2) mean body mass of goslings near fledging was low, or (3) mean fledging date was late. Our results suggest that migration survival of young is affected by a combination of several factors (climatic conditions, body mass, and fledging date) and that survival is reduced when one of those factors intervenes.
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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 | 0.000 | 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) | 0.000 | 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".