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Record W2289695157 · doi:10.1594/pangaea.817049

Contaminant concentrations in ivory gull (Pagophila eburnea) eggs from Svalbard and the Russian Arctic (2006-2007)

2009· dataset· en· W2289695157 on OpenAlexaboutno aff
Cecilie Miljeteig, Hallvard Strøm, Maria Gavrilo, Andrey Volkov, Bjørn Munro Jenssen, Geir Wing Gabrielsen

Bibliographic record

VenueFigshare · 2009
Typedataset
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticEnvironmental scienceGeographyFisheryOceanographyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

We found high levels of contaminants, in particular organochlorines, in eggs of the ivory gull Pagophila eburnea, a high Arctic seabird species threatened by climate change and contaminants. An 80% decline in the ivory gull breeding population in the Canadian Arctic the last two decades has been documented. Because of the dependence of the ivory gull on sea ice and its high trophic position, suggested environmental threats are climate change and contaminants. The present study investigated contaminant levels (organochlorines, brominated flame retardants, perfluorinated alkyl substances, and mercury) in ivory gull eggs from four colonies in the Norwegian Svalbard) and Russian Arctic (Franz Josef Land and Severnaya Zemlya). The contaminant levels presented here are among the highest reported in Arctic seabird species, and we identify this as an important stressor in a species already at risk due to environmental 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.190
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.226
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2009
Admission routes1
Has abstractyes

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