Mercury biomagnification in the food webs of acidic lakes in Kejimkujik National Park and National Historic Site, Nova Scotia
Bibliographic record
Abstract
Mercury (Hg) concentrations in fish from acidic lakes (pH < 6.0) are typically elevated above those from near-neutral systems. It is unknown whether high biomagnification rates through the supporting food web can explain elevated Hg concentrations in top predators from low pH lakes. To investigate this, we collected yellow perch ( Perca flavescens ), brown bullhead ( Ameiurus nebulosus ), banded killifish ( Fundulus diaphanous ), golden shiner ( Notemigonus crysoleucas ), and littoral and pelagic invertebrates from four acidic lakes in Kejimkujik National Park and Historic Site (KNPNHS), Nova Scotia, Canada, and analyzed them for total Hg and methyl Hg (MeHg), and δ13C and δ15N to determine sources of energy and trophic position, respectively. Mercury biomagnification rates (slopes of log Hg versus δ15N) varied significantly among the four lakes but did not explain the among-lake differences in perch Hg; these slopes were also within the range published for near-neutral systems. Rather, Hg concentrations in yellow perch (i.e., predatory fish) in KNPNHS were higher in lakes with higher MeHg in lower-trophic-level organisms and suggest that processes influencing Hg uptake at the base of the food web are more important than rates of food web biomagnification for understanding the variation in concentrations of this contaminant among top predators.
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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 | 0.001 |
| 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) | 0.001 | 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".