Forest fire increases mercury accumulation by fishes via food web restructuring and increased mercury inputs
Bibliographic record
Abstract
Recent findings indicate that fishes from lakes in partially burned catchments contain greater mercury (Hg) concentrations than fishes from reference catchments. Increased methyl Hg (MeHg) concentrations in fishes can result in serious health problems for consumers. Here we show that a forest fire caused a 5-fold increase in whole-body Hg accumulation by rainbow trout (Oncorhynchus mykiss) and smaller Hg increases in muscle of several fish species in a mountain lake. The enhanced Hg accumulation was caused primarily by increased nutrient concentrations in the lake, which enhanced productivity and restructured the food web through increased piscivory and consumption of Mysis. This restructuring resulted in increases to the trophic positions and Hg concentrations of fishes. Forest fire also caused a large short-term release of total Hg (THg) and MeHg to streams and the lake. This release initiated a small pulse of MeHg in invertebrates that contributed to enhanced Hg accumulation by fishes. Climate change and prescribed burning to compensate for past fire suppression are predicted to increase future forest fire occurrence in North America, and increased Hg accumulation by fishes may be an unexpected consequence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".