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Record W2140065346 · doi:10.1093/icesjms/fss040

Discontinuous change in ice cover in Hudson Bay in the 1990s and some consequences for marine birds and their prey

2012· article· en· W2140065346 on OpenAlexaffabout
Anthony J. Gaston, Paul A. Smith, Jennifer F. Provencher

Bibliographic record

VenueICES Journal of Marine Science · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of VictoriaEnvironment and Climate Change Canada
Fundersnot available
KeywordsCapelinBayArcticSeabirdSea iceClimate changeMarine ecosystemOceanographyFisheryPredationGeographyArctic ice packPopulationEcosystemEcologyEnvironmental scienceBiologyGeologyDemography

Abstract

fetched live from OpenAlex

Abstract Gaston A. J., Smith, P. A., and Provencher, J. F. 2012. Discontinuous change in ice cover in Hudson Bay in the 1990s and some consequences for marine birds and their prey. – ICES Journal of Marine Science, 69: . Arctic ice cover has changed strikingly since the mid-1990s, with the minimum ice extent in the northern hemisphere diminishing by 8.5% per decade since 1981. In the Canadian Arctic, ice cover in June and November showed a step change in the mid-1990s, with little reduction before that. There was a similar step change in northern Hudson Bay. A long-term dataset on marine birds at Coats Island, Nunavut, revealed that many changes in seabird biology also exhibited an abrupt change at, or soon after, the change in ice conditions. This applied to their diet that switched in the 1990s from one dominated by Arctic cod, Boreogadus saida, to one dominated by capelin, Mallotus villosus. Evidence from the proportion of Arctic cod in adult diets suggested that the length of the open-water season may be a good predictor of the switch between Arctic cod and capelin. Other changes, in nestling growth and population trend, may relate to the same ecosystem changes that led to the switch in diet. Abrupt changes, as in the breeding biology of murres at Coats Island, would seem to be characteristic of ecosystem alterations driven by climate change.

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)0.003
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 score0.041
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations67
Published2012
Admission routes2
Has abstractyes

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