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Record W1984054484 · doi:10.1021/es1018114

Increasing Mercury in Yellow Perch at a Hotspot in Atlantic Canada, Kejimkujik National Park

2010· article· en· W1984054484 on OpenAlexafffundabout
Brianna Wyn, Karen A. Kidd, Neil M. Burgess, R. Allen Curry, Kelly R. Munkittrick

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaParks Canada
KeywordsPerchMercury (programming language)National parkFisheryNova scotiaRecreational fishingBioaccumulationEnvironmental scienceFishingGeographyBiologyEcologyFish <Actinopterygii>Archaeology

Abstract

fetched live from OpenAlex

In the mid-1990s, yellow perch (Perca flavescens) and common loons (Gavia immer) from Kejimkujik National Park and National Historic Site (KNPNHS), Nova Scotia, Canada, had among the highest mercury (Hg) concentrations across North America. In 2006 and 2007, we re-examined 16 lakes to determine whether there have been changes in Hg in the loon's preferred prey, yellow perch. Total Hg concentrations were measured in up to nine perch in each of three size classes (5-10 cm, 10-15 cm, and 15-20 cm) consumed by loons. Between 1996/97 and 2006/07, polynomial regressions indicated that Hg in yellow perch increased an average of 29% in ten lakes, decreased an average of 21% in three, and were unchanged in the remaining three lakes. In 2006/07, perch in 75% of the study lakes had Hg concentrations (standardized to 12-cm fish length) equal to or above the concentration (0.21 μg·g(-1) ww) associated with a 50% reduction in maximum productivity of loons, compared with only 56% of these lakes in 1996/97. Mercury contamination currently poses a greater threat to loon health than a decade ago, and further reductions in anthropogenic emissions should be considered to reduce its impacts on ecosystem 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations50
Published2010
Admission routes3
Has abstractyes

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