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Record W2052735331 · doi:10.1039/c2em30687e

Temporal trends of mercury in Greenland ringed seal populations in a warming climate

2012· article· en· W2052735331 on OpenAlexaff
Frank F. Rigét, Runé Dietz, Keith A. Hobson

Bibliographic record

VenueJournal of Environmental Monitoring · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change Canada
FundersMiljøstyrelsenAarhus Universitet
KeywordsMercury (programming language)North Atlantic oscillationTrophic levelEnvironmental scienceGroenlandiaClimatologyOceanographyGreenland ice sheetIce coreArctic oscillationClimate changePhysical geographyGeographyEcologyGeologyBiologyIce sheetNorthern Hemisphere

Abstract

fetched live from OpenAlex

Temporal trends of mercury in livers of ringed seals collected from the early 1980s to 2010 from central West, Northwest and central East Greenland were studied. In this period the climate of Greenland warmed and the influences of climate indices such as ice coverage, water temperature and the Atlantic Oscillation Index on mercury concentration were evaluated using multiple regressions and Akaike's Information Criteria (AIC) to determine the most parsimonious models. Biological co-variables such as age, sex and trophic position (as determined by stable isotope analysis) of seals were also evaluated. Increasing levels of mercury in seals were found in Ittoqqortoormiit, central East Greenland, and Avanersuaq, Northwest Greenland, with an annual increase of +10.3 and +2%, respectively. Age was an important co-variable for all three regions and trophic position for two regions. The Atlantic Oscillation Index was also an important explanatory variable for all three regions and was positively associated with mercury concentrations in seals indicating the importance of global climatic processes on ringed seal populations in Greenland.

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 score0.013
Threshold uncertainty score0.523

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.293
Teacher spread0.249 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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