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Record W2015489694 · doi:10.1021/es7025938

Increasing Perfluoroalkyl Contaminants in East Greenland Polar Bears (<i>Ursus maritimus</i>): A New Toxic Threat to the Arctic Bears

2008· article· en· W2015489694 on OpenAlexfundno aff
Runé Dietz, Rossana Bossi, Frank F. Rigét, Christian Sonne, Erik W. Born

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
FundersComisión de Investigaciones CientíficasLundbeckfondenCanada Research Chairs
KeywordsUrsus maritimusArcticThe arcticPolarEnvironmental chemistryPerfluorooctaneAnimal scienceChemistryEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

A well-defined subsample of 128 subadult (3-5 years) polar bears (Ursus maritimus) from 19 sampling years within the period 1984-2006 was investigated for perfluoroalkyl contaminants (PFCs). Linear regression analysis of logarithmic-transformed median concentrations showed significant annual increases for PFOS (4.7%), PFNA (6.1%), PFUnA (5.9%), PFDA (4.3%), PFTrA (8.5%), PFOA (2.3%), and PFDoA (5.2%). For four of the PFCs, a LOESS smoother model provided significantly better descriptions, revealing steeper linear annual increases for PFOSA of 9.2% after 1990 and between 18.6 and 27.4% for PFOS, PFDA, and PFTrA after 2000. Concentrations of SigmaPFCs, by 2006, exceeded the concentrations of all conventional OHCs (organohalogen compounds), of which several have been documented to correlate with a number of negative health effects. If the PFC concentrations in polar bears continue to increase with the steepest observed trends, then the lowest no-adverse-effect level (NOAEL) and lowest-adverse-effect level (LOAEL) detected for rats and monkeys will be exceeded in 2014-2024. In addition, the rapidly increasing concentrations of PFCs are likely to cause cumulative and combined effects on the polar bear, compounding the already detected threats from OHCs.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations140
Published2008
Admission routes1
Has abstractyes

Explore more

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