Trends in the harvest of Brünnich's guillemots Uria lomvia in Newfoundland: effects of regulatory changes and winter sea ice conditions
Bibliographic record
Abstract
Abstract The harvest of Brünnich's guillemots (thick‐billed murres) Uria lomvia off Newfoundland and Labrador is the only legal hunt of seabirds by non‐natives in Canada and the United States. Ringing programmes at Arctic breeding colonies have been used to track changes in numbers and age composition of harvested birds. In recent years, the numbers of rings reported by hunters have fallen steeply. We examined recoveries by hunters of rings from a colony in northern Hudson Bay during 1984‐2006 to assess the possible reasons for the decline in recoveries. Because recoveries of common guillemots have remained stable over the same period, it seems unlikely that a change in reporting rates is involved. Instead, it appears that a combination of a reduction in hunting pressure and a change in the behaviour of the birds due to more northerly termination of the winter pack‐ice boundary account for the observed reduction in recovery rates. The pattern of first year recovery rates, in particular, appears consistent with an explanation based on accommodation to ice conditions. Recovery rates of older birds appear less affected by ice conditions and in recent years, have, in any case, been very low. Our study demonstrates that under conditions of changing weather and climate, harvest management decisions may not always have the impact expected.
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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 | 0.001 |
| 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".