Mercury Biomagnification through Food Webs Is Affected by Physical and Chemical Characteristics of Lakes
Bibliographic record
Abstract
Mercury (Hg) contamination in aquatic systems remains a global concern because the organic form, methyl Hg (MeHg), can biomagnify to harmful concentrations in fish, fish-eating wildlife, and humans. Food web transfer of MeHg has been explored using models of log MeHg versus relative trophic position (nitrogen isotopes, δ(15)N), but regression slopes vary across systems for unknown reasons. In this study, MeHg biomagnification was determined for 11 lake food webs in Kejimkujik National Park, Nova Scotia, Canada, and compared to physical and chemical lake characteristics using principal component and multiple regression analyses. MeHg biomagnification (regression slopes of log MeHg versus baseline-adjusted δ(15)N for fishes and invertebrates) varied significantly across lakes and was higher in systems with lower aqueous nutrient/MeHg/chloride scores. This is one of the largest, consistent data sets available on MeHg biomagnification through temperate lake food webs and the first study to use a principal component and multiple regression approach to understand how lake chemical and physical characteristics interact to affect biomagnification among systems. Overall, our results show that the magnitude of MeHg biomagnification through lake food webs is related to the chemical and physical characteristics of the systems, but the underlying mechanisms warrant further investigation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".