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Record W2012777744 · doi:10.7589/0090-3558-37.4.693

CONCENTRATIONS AND INTERACTIONS OF SELECTED ESSENTIAL AND NON-ESSENTIAL ELEMENTS IN BOWHEAD AND BELUGA WHALES OF ARCTIC ALASKA

2001· article· en· W2012777744 on OpenAlexaboutno aff
Victoria Woshner, Todd M. O’Hara, Gerald R. Bratton, Robert Suydam, Val R. Beasley

Bibliographic record

VenueJournal of Wildlife Diseases · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersOffice of Naval Research
KeywordsBeluga WhaleBelugaLeucasMercury (programming language)BiologySeleniumCadmiumArcticZoologyCetaceaWhaleEnvironmental chemistryAnimal scienceFisheryEcologyChemistry

Abstract

fetched live from OpenAlex

In this study, we evaluated concentrations of twelve essential and non-essential elements (As, Cd, Co, Cu, Pb, Mg, Mn, Hg, Mo, Se, Ag, and Zn) in tissues of bowhead (Balaena mysticetus) and beluga (Delphinapterus leucas) whales from arctic Alaska (USA) and northwestern Canada. Tissue samples were collected between 1983 and 1997, mostly in 1995-97. The essential elements are reported to develop reference ranges for health status determination, and to help assess known or suspected interactions affecting toxicoses of cadmium (Cd) and mercury (Hg). In some tissues, Cd, Hg, and selenium (Se) were present at concentrations that have been associated with toxicoses in some domestic animals. Nevertheless, tissue levels of all elements were within ranges that have been reported previously in marine mammals. While mean Ag concentrations in beluga whale liver were relatively high (15.91 micrograms/g ww), Ag was not associated with hepatic Se levels or age, contrary to previous findings. Significant associations included: Cd with age, Zn, or Cu; Cu with age, Zn or Ag; and Hg with age, Se, Zn, or Cu. This study found hepatic Hg:Se molar ratios to be consistently lower than unity and different between species. Possible explanations for observed elemental correlations (i.e., interactions) and ancillary mechanisms of Cd and Hg detoxification are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.265

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)0.0000.000

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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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

Citations94
Published2001
Admission routes1
Has abstractyes

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