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Record W2140426241 · doi:10.1016/j.icesjms.2006.04.004

Seabird numbers and prey consumption in the North Atlantic

2006· article· en· W2140426241 on OpenAlexaffabout
Robert T. Barrett, Gilles Chapdelaine, Tycho Anker‐Nilssen, Anders Mosbech, William A. Montevecchi, Richard R. Veit

Bibliographic record

VenueICES Journal of Marine Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSeabirdFisheryFishingGeographyPredationApex predatorEcologyOceanographyBiology

Abstract

fetched live from OpenAlex

Abstract We compared seasonal composition, abundance, and biomass of seabirds between the Northeast (ICES region) and Northwest (NAFO region) Atlantic fisheries regions to identify differences in community assemblage and prey consumption. Seabirds were more abundant in the Northwest Atlantic, but biomass was greater in the Northeast. This disparity resulted from enormous numbers of little auks Alle alle breeding in West Greenland and of Leach's storm-petrels Oceanodroma leucorhoa breeding in Newfoundland, plus large numbers of non-breeding shearwaters Puffinus spp. entering southern NAFO areas in summer. The Northeast Atlantic communities were dominated numerically by northern fulmars Fulmarus glacialis, large auks Uria spp., and the Atlantic puffin Fratercula arctica. Seabirds occupying the North Atlantic consume approximately 11 × 106 t of food annually. Overall consumption rates peak during summer as a result of increased breeding activity and seasonal movements of birds into the North Atlantic. Because of the greater biomass of birds in the northeast, consumption (mainly by piscivores) in ICES areas was approximately 20% higher than that in NAFO areas, where planktivores dominate. NAFO areas had, however, a much greater consumption rate per unit area than ICES areas. Comparative studies such as these could prove informative in assessing large predator responses to the influence of fishing and ocean-scale 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)0.001
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 score0.006
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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
Published2006
Admission routes2
Has abstractyes

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