Mercury concentrations in northern pike ( <i>Esox lucius</i>) from boreal lakes with logged, burned, or undisturbed catchments
Bibliographic record
Abstract
We measured total Hg and stable isotopes (δ13C and δ15N) 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".