The Single and Combined Effects of Wildfire Runoff and Sediment-Bound Copper on the Freshwater Amphipod <i>Hyalella azteca</i>
Bibliographic record
Abstract
The frequency of wildfire is expected to increase with time as a function of climate change. Recent studies in our laboratory have demonstrated that pyrogenic polycyclic aromatic hydrocarbons can cause greater-than-additive effects in Hyalella azteca in the presence of low concentrations of Cu. We hypothesized that freshwater animals inhabiting Cu-contaminated sites, such as those in the vicinity of Cu mines, may be vulnerable to nonadditive toxicity from contaminants released by wildfires. To investigate the interaction between Cu and water conditioned by burnt wood ash (fire extract), we exposed H. azteca for 14 d to binary mixtures of 225 mg/kg Cu-enriched artificial sediment (225 mg Cu/kg) and a fire extract dilution series (12.5, 25, 50, 75, and 100%). All binary mixtures of Cu-enriched sediment and fire extract resulted in complete mortality with the exception of Cu-enriched sediment + 12.5% fire extract. The combination of Cu-enriched sediment with 12.5% fire extract had a more-than-additive effect on survival and tissue Cu concentration, but there was no reduction in growth or acetylcholinesterase activity compared to the 225 mg/kg Cu-contaminated sediment or fire extract control, respectively. Acetylcholinesterase activity was significantly reduced in amphipods exposed to fire extract, but the presence of Cu did not exacerbate this effect. The results suggest that Cu-contaminated water bodies that receive runoff from wildfires are at risk of enhanced toxicity. Environ Toxicol Chem 2020;39:1988-1997. © 2020 SETAC.
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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".