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Record W3096676478 · doi:10.1093/condor/109.4.894

Does Sea Ice Constrain the Breeding Schedules of High Arctic Northern Fulmars?

2007· article· en· W3096676478 on OpenAlexaffabout
Mark L. Mallory, Mark R. Forbes

Bibliographic record

VenueOrnithological Applications · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsArcticSeabirdSea iceEcologyBiologyPhenologyArctic ice packSeasonal breederRange (aeronautics)GeographyPredation

Abstract

fetched live from OpenAlex

Abstract The Northern Fulmar (Fulmarus glacialis) is a pelagic seabird that breeds across 25° of latitude, from the boreal to the high Arctic oceanographic zones. We examined the breeding schedule of fulmars in the remote Cape Vera colony in the Canadian high Arctic, a marine region covered by sea ice much of the year, to determine if the timing of breeding and colony attendance patterns of birds differed from the breeding phenology of fulmars in colonies farther south. Cape Vera fulmars arrived at the colony later in the year, spent less time at the colony before egg-laying, and took a significantly longer prelaying exodus from the colony compared to fulmars nesting in more southerly colonies. After egg-laying, however, patterns of colony attendance by fulmars in the high Arctic were similar to patterns for fulmars in southern colonies; this part of the fulmar breeding schedule may be inflexible. The differences in breeding schedules across the species' range might reflect behavioral adaptations by arctic-nesting birds to accommodate the physical and biological limitations imposed by extensive sea ice near arctic colonies, particularly early in the breeding season. Given that climate warming and corresponding reductions in sea ice are taking place in the Arctic, it remains to be determined whether fulmars in the high Arctic have the behavioral flexibility in their breeding phenology to compensate for rapidly occurring changes in their environment.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

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)0.0020.001

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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.

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

Citations18
Published2007
Admission routes2
Has abstractyes

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