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Record W4235816554 · doi:10.1093/auk/120.2.362

Synchronous Fluctuations of Thick-Billed Murre (Uria Lomvia) Colonies in The Eastern Canadian Arctic Suggest Population Regulation in Winter

2003· article· en· W4235816554 on OpenAlexaboutno aff
Anthony J. Gaston

Bibliographic record

VenueThe Auk · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsArcticPopulationBiologyRange (aeronautics)EcologyPopulation declineZoologyGeographyDemographyHabitat

Abstract

fetched live from OpenAlex

Abstract Trends in Thick-billed Murre (Uria lomvia) populations at Prince Leopold Island and Coats Island, Nunavut—colonies at opposite ends of the species range in the eastern Arctic—were compared over the period 1985–2000. Population trends were monitored by daily counts of fixed study plots on six or more days each year. At Coats Island, annual mean counts were well correlated with numbers of breeding pairs located on separate breeding study areas, suggesting that the monitoring counts provided a useful index of the breeding population. Overall, counts at both colonies increased over the period of observations (by 2.1% annually at Coats Island and 1.5% at Prince Leopold Island), but a period of significant decline occurred during 1989–1991 and numbers remained stable after 1998. Fluctuations at the two colonies were well-synchronized. Changes in numbers from year-to-year were positively correlated with the mean mass of breeders during the first half of incubation. Hence, birds appeared to be in poorer condition in years when the population decreased. The similarity in fluctuations at colonies as far apart as Coats and Prince Leopold islands suggests that population changes may be determined by events on the common wintering grounds. The correlation between changes in counts and body mass at Coats Island suggests that the common factor may be one that affects the availability of food during the nonbreeding period.

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 score0.705
Threshold uncertainty score0.958

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.232
Teacher spread0.220 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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