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Record W4412374704 · doi:10.56178/eh.v40i1.1511

First GPS record of a Red-legged Cormorant off the coast of Peru suggests pelagic foraging behavior

2025· article· en· W4412374704 on OpenAlexafffund
Francis van Oordt, Jaime Silva, Fritz Hertel, Kyle H. Elliott

Bibliographic record

VenueEl Hornero · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPelagic zoneForagingSeabirdNest (protein structural motif)GeographyFisheryCormorantHabitatPopulationEcologyTunaBiologyFish <Actinopterygii>Predation

Abstract

fetched live from OpenAlex

Red-legged Cormorants (Poikilocarbo gaimardi) are distributed along the Pacific coast of South America, from Peru to Chile, and restricted to southernmost Argentina along the Atlantic coast. Population monitoring of this species in the Pacific is scarce and research on their ecology has been limited to Argentina, where it has been considered a rocky bottom feeder, travelling distances of ~3 km away from the nest. We deployed and retrieved a GPS device on one adult Red-legged Cormorant at Punta Atico, Arequipa, Peru, in June 2018. Foraging trips of the tracked individual were on average ~6 km away from the nest and reached a maximum of 10 km. More than 50% of trips occurred in pelagic habitats and were widely dispersed around the nest. Based on the observation of this individual we suggest that in the productive Peruvian Humboldt Current System, Red-legged Cormorants may be pelagic foragers rather than benthic specialists. These results have important implications for conservation strategies aimed to protect this seabird species along the Peruvian coast.

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 score0.017
Threshold uncertainty score1.000

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)0.0010.000

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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