Winter Distribution, Movements, and Annual Survival of Radiomarked Vancouver Canada Geese in Southeast Alaska
Bibliographic record
Abstract
ABSTRACT Management of Pacific Flyway Canada geese (Branta canadensis) requires information on winter distribution of different populations. Recoveries of tarsus bands from Vancouver Canada geese (B. canadensis fulva) marked in southeast Alaska, USA, ≥4 decades ago suggested that ≥83% of the population was non‐migratory and that annual adult survival was high (Ŝ = 0.836). However, recovery distribution of tarsus bands was potentially biased due to geographic differences in harvest intensity in the Pacific Flyway. Also, winter distribution of Vancouver Canada geese could have shifted since the 1960s, as has occurred for some other populations of Canada geese. Because winter distribution and annual survival of this population had not recently been evaluated, we surgically implanted very high frequency radiotransmitters in 166 adult female Canada geese in southeast Alaska. We captured Vancouver Canada geese during molt at 2 sites where adults with goslings were present (breeding areas) and 2 sites where we observed nonbreeding birds only. During winter radiotracking flights in southeast Alaska, we detected 98% of 85 females marked at breeding areas and 83% of 70 females marked at nonbreeding sites, excluding 11 females that died prior to the onset of winter radiotracking. We detected no radiomarked females in coastal British Columbia, or western Washington and Oregon, USA. Most (70%) females moved ≤30 km between November and March. Our model‐averaged estimate of annual survival (Ŝ = 0.844, SE = 0.050) was similar to the estimate of annual survival of geese marked from 1956 to 1960. Likely <2% of Vancouver Canada geese that nest in southeast Alaska migrate to winter areas in Oregon or Washington where they could intermix with Canada geese from other populations in the Pacific Flyway. Because annual survival of adult Vancouver Canada geese was high and showed evidence of long‐term consistency, managers should examine how reproductive success and recruitment may affect the population.
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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) | 0.001 | 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".