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Record W2081405667 · doi:10.2981/09-020

Trends in the harvest of Brünnich's guillemots Uria lomvia in Newfoundland: effects of regulatory changes and winter sea ice conditions

2010· article· en· W2081405667 on OpenAlexafffundabout
Anthony J. Gaston, Gregory J. Robertson

Bibliographic record

VenueWildlife Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change CanadaCarleton University
FundersEnvironment CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsArcticBayGeographyArctic ice packBiologyFisheryEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.254
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2010
Admission routes3
Has abstractyes

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