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Record W4415727649 · doi:10.2478/orhu-2025-0032

Diet of a breeding population of South Polar Skuas <i>(Stercorarius maccormicki)</i> of the Schirmacher Oasis, Central Dronning Maud Land, East Antarctica

2025· article· en· W4415727649 on OpenAlexfundno aff
Sergey Golubev

Bibliographic record

VenueOrnis Hungarica · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsnot available
FundersAlberta Agricultural Research Institute
KeywordsContext (archaeology)PopulationPredationSeasonal breederHabitat

Abstract

fetched live from OpenAlex

Abstract The first detailed study on the diet of a breeding population of South Polar Skuas (Stercorarius maccormicki) at Schirmacher Oasis is based on the collection and analysis of prey remains and pellets from the 2024/2025 breeding season and food samples from previous seasons near active skua nests (n=8) and old breeding territories (n=2). The diet composition of the skua population and its changes in the context of human activity were determined. The diet included five bird species, fish, squid, and food scraps and garbage from Antarctic stations and bases. The diet was dominated by Snow Petrels (Pagodroma nivea) , with subdominants being Antarctic Petrels (Thalassoica antarctica) and Adélie Penguins (Pygoscelis adeliae) . Native marine fish and squid were likely incidental dietary components, introduced into pellets along with the skuas’ prey. Human activities have influenced the diet of skuas on a population-wide scale by introducing imported food and garbage into their diet. Food waste was found in seven of eight surveyed breeding territories and in one of two old breeding territories. Diet composition and behaviour of skuas in areas of human activity in Antarctica can be used as an indicator of the quality of local human waste management, as well as an indicator of the health of the ecosystem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.208
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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