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Record W2162665014 · doi:10.14430/arctic3335

Glaucous Gull Predation on Dovekies: Three New Hunting Methods

2010· article· en· W2162665014 on OpenAlexvenueno aff
Dariusz Jakubas, Katarzyna Wojczulanis‐Jakubas

Bibliographic record

VenueARCTIC · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsForagingLarusPredationFisheryBiologyEcologyGeographyZoologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We describe three previously unreported methods that hunting glaucous gulls (Larus hyperboreus) use to capture nesting and fledgling dovekies (Alle alle). During the nesting period, the pale-gray and white gulls camouflaged themselves by perching with head lowered on remnant snow patches in the dovekie colony, trying to ambush flying adults. We observed two other glaucous gull hunting methods on the open fjord water after the dovekie fledglings had left the colony. Gulls approached young dovekies in a fast, low-level glide, presumably to surprise the prey, and attempted to snatch them from the water. Gulls also swam rapidly towards young dovekies, zigzagging among small ice floes, presumably to confuse the birds and catch them before they could dive. The methods described, representing technical foraging innovations, supplement the evidence that gulls are a bird family that displays a diverse foraging innovation repertoire.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.017
GPT teacher head0.301
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes1
Has abstractyes

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