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Record W4240893532 · doi:10.1139/f00-126

Mercury concentrations in northern pike ( <i>Esox lucius</i>) from boreal lakes with logged, burned, or undisturbed catchments

2000· article· en· W4240893532 on OpenAlexvenueno aff
Édenise Garcia, Richard Carignan

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsEsoxPikeMercury (programming language)Environmental scienceTrophic levelBorealZooplanktonHydrology (agriculture)Environmental chemistryEcologyFisheryChemistryBiologyGeologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We measured total Hg and stable isotopes (δ 13 C and δ 15 N) in northern pike (Esox lucius) from 19 Boreal Shield lakes with undisturbed, logged, or burned watersheds. Average Hg level in standard 560-mm northern pike, on a dry weight basis, was significantly higher in logged lakes (3.4 µg·g -1 ) than in reference lakes (1.9 µg·g -1 ). Average Hg concentrations in burned lakes (3.0 µg·g -1 ) did not differ significantly from those in logged and references lakes. Concentrations of Hg normalized to trophic position determined from isotopic composition yielded similar results. Mercury levels were above the WHO safe consumption limit in all logged lakes. Mercury in northern pike was correlated with methyl mercury in zooplankton (+), total N (+), pH (-), alkalinity (-), sulfate (+), dissolved organic C loading (+), and light attenuation in lake water (+). Stepwise multiple regressions explained 79% of the variability in Hg in fish and included methyl mercury in zooplankton, pH, and sulfate as independent variables. Explained variability increased to 92% when a second-order lake with an exceptionally large drainage area was excluded. Our results suggest that extensive logging activities may disrupt the natural cycling of Hg in watersheds and increase Hg levels in the aquatic biota.

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 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 score0.677
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations90
Published2000
Admission routes1
Has abstractyes

Explore more

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