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Record W2096963039 · doi:10.1139/er-2013-0072

Mercury and marine birds in Arctic Canada: effects, current trends, and why we should be paying closer attention

2014· article· en· W2096963039 on OpenAlexafffundvenueabout
Jennifer F. Provencher, Mark L. Mallory, Birgit M. Braune, Mark R. Forbes, H. Grant Gilchrist

Bibliographic record

VenueEnvironmental Reviews · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsAcadia UniversityCarleton University
FundersAboriginal Affairs and Northern Development CanadaAssociation of Canadian Universities for Northern StudiesAcadia UniversityNatural Sciences and Engineering Research Council of CanadaArctic Institute of North America
KeywordsMercury (programming language)BiomonitoringWildlifeMarine ecosystemEcosystemArcticEcosystem healthEcologyEnvironmental scienceSentinel speciesBiologyEcosystem services

Abstract

fetched live from OpenAlex

Mercury (Hg) is a naturally occurring trace element that is also a by-product of anthropogenic activities and, in its methylated form, it is a neurotoxin that can have adverse effects on wildlife. The toxicity of Hg for humans, wildlife, and ecosystem health merits monitoring of its concentrations by various sampling means. Marine birds are widely used as indicators of ecosystem health, including biomonitoring of Hg in the Arctic. Since the mid-1970s, Hg concentrations in marine birds have been monitored across the Canadian North. Current Hg burdens in most northern marine bird species are below levels associated with health concerns, but several species have concentrations that are at or near levels associated with impaired reproduction. Arctic marine birds in Canada may be particularly at risk from increasing Hg levels associated with changing climatic conditions and long-term Hg deposition patterns. Research on marine birds should, therefore, continue to focus on spatial and temporal patterns of Hg contamination, assessing levels and biological effects in species that are experiencing high concentrations, and among species that are widely harvested due to the possible implications for human health.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.985

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 head0.024
GPT teacher head0.265
Teacher spread0.240 · 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

Citations63
Published2014
Admission routes4
Has abstractyes

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