Recent Trends in First-Year Survival for Black Brant Breeding in Southwestern Alaska
Bibliographic record
Abstract
We estimated first-year and adult survival of Black Brant (Branta bernicla nigricans) from the Yukon—Kuskokwim Delta, Alaska, to assess (1) the role that first-year survival plays in declining recruitment and in the local breeding population's decline since the early 1980s and (2) the potential role of subsistence harvest in declining first-year survival. We used band-recovery models in program Mark to estimate band-recovery rates and annual survival from 1986 to 2007. The only models of band recoveries that received support contained annual variation and an additive effect of sex on band-recovery rates. The two best-supported models of annual survival differentiated between first-year and older Black Brant. The best-supported model (Akaike weight = 0.69) included a linear trend in first-year survival, while the second best-supported model (Akaike weight = 0.20) included an effect of mean gosling mass on first-year survival. Band-recovery rates corresponded to harvest rates of ∼1%, indicating that during the study period harvest was not demographically important. Adult survival was comparable (0.87) to that from other studies of this population, while first-year survival declined from 0.46 in 1986 to 0.24 in 2007. The trend of decline in first-year survival represented the effects of variation in conditions for goslings' growth in the breeding area combined with unknown effects on migration and in the wintering areas. Declining first-year survival is an important contributor to decline in the local breeding population of Black Brant.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".