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Record W2064557216 · doi:10.1021/es7024388

Size and Biomagnification: How Habitat Selection Explains Beluga Mercury Levels

2008· article· en· W2064557216 on OpenAlexafffund
Lisa L. Loseto, Gary A. Stern, Steven H. Ferguson

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaArcticNetUniversity of Winnipeg
KeywordsBeluga WhaleBelugaBiomagnificationBiologyBioaccumulationPopulationMercury (programming language)Beaufort seaCetaceaFatty acidZoologyEcologyBiochemistryArctic

Abstract

fetched live from OpenAlex

Mercury (Hg) levels in the Beaufort Sea beluga (Delphinapterus leucas) population increased during the 1990s; levels have since declined but remain higher than the 1980s. The diet of this beluga population is not well-known, thus it is difficult to assess dietary Hg sources. During the summer, the Beaufort Sea belugas segregate by length, sex, and reproductive status corresponding to habitat use that may result in feeding differences and ultimately Hg uptake. To test this hypothesis, we examine beluga dietary variation using fatty acid profiles and determine which biological variables best predict diet Relationships between biological variables and fatty acids were further evaluated with stable isotopes and Hg concentrations in liver and muscle. Hg concentrations in muscle were better related to liver delta15N than muscle delta15N. Stable isotopes and fatty acids are compared in their ability to describe dietary Hg processes in beluga. Fatty acids provided support for influences of whale behavior on dietary Hg uptake, whereas stable isotopes inferred tissue Hg metabolic rates. Here, we show beluga length drives diet variability leading to differences in Hg uptake and biomagnification processes dominate beluga Hg levels over Hg bioaccumulation over time.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.233
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 teacher head, not a consensus.

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

Citations93
Published2008
Admission routes2
Has abstractyes

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