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Mercury in eastern coyotes from Nova Scotia, Canada: Effects of geography and trophic position

2025· article· en· W4408941132 on OpenAlexafffundabout
Mark L. Mallory, Julia E. Baak, Michael Boudreau, Jenna Marie Priest, André Morrill, Jennifer F. Provencher, Nelson J. O’Driscoll

Bibliographic record

VenueThe Science of The Total Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change CanadaNova Scotia Department of AgricultureAcadia University
FundersAcadia University
KeywordsNova scotiaMercury (programming language)GeographyTrophic levelNova (rocket)EcologyEnvironmental scienceArchaeologyBiology

Abstract

fetched live from OpenAlex

Mercury (Hg) is a global environmental concern due to its wide distribution and myriad of deleterious effects on biota. We studied hepatic Hg in a widespread, top predator in the terrestrial ecosystem of Nova Scotia, Canada, the eastern coyote (Canis latrans), to determine recent concentrations, identify drivers of variation in Hg levels, and assess the utility of this species as a mercury biomonitor for this ecosystem. Coyotes feeding at higher trophic levels, and those in the south and east of the province, had higher Hg concentrations, but there was high variability within and among age-sex groupings. We conclude that coyotes may be useful biomonitors at larger regional scales (e.g., the Maritimes), but we recommend additional research on fishers (Pekania pennanti), a species which we used to compare to coyotes, and for which trophic position and Hg concentrations were surprisingly high at smaller scales within Nova Scotia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.199
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes3
Has abstractyes

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