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Record W3002956549 · doi:10.1139/as-2019-0017

Changes in the distribution of nesting Arctic seaducks are not strongly related to variation in polar bear presence

2020· article· en· W3002956549 on OpenAlexaffvenueabout
Cody J. Dey, Christina A. D. Semeniuk, Samuel A. Iverson, H. Grant Gilchrist

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Windsor
Fundersnot available
KeywordsEiderArcticEcologyPredationGeographySpatial variabilitySpatial ecologyPolarPopulationPhysical geographyBiologyDemography

Abstract

fetched live from OpenAlex

Contemporary climate change is predicted to expose some species to altered predation regimes. Losses of Arctic sea ice are causing polar bears to increasingly forage on colonial seaduck eggs in lieu of ice-based hunting of marine mammals. Although polar bear predation of bird eggs has now been widely documented, it is unclear whether this change in predator behavior is having population-level consequences for Arctic breeding birds. In this study, we tested whether changes in the number of common eider nests on 76 islands in Hudson Strait, Canada, were related to variation in polar bear presence. We found that polar bear sign detected during eider breeding surveys was strongly correlated with spatial patterns of polar bears observed during aerial surveys. However, changes in eider nest count did not appear to be clearly related to polar bear sign at either the island scale or the island-cluster scale. This results of this study, therefore, suggest that the spatial overlap between eiders and polar bears varies across the landscape, but patterns of polar bear spatial variation do not seem to have driven large-scale redistribution of nesting common eiders.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.012
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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