Applying eco-indicator sensitivity distribution to evaluate chlorpyrifos risk in grassland soils with prescribed fire and grazing history
Bibliographic record
Abstract
Prescribed fire and grazing can enhance soil ecosystem functions in grasslands but may also induce ecological stress on soil function, making soil more vulnerable to chemical stressors like pesticides, which contaminate grasslands via aerial spray drift. Therefore, this study investigated the effects of prescribed fire-grazing history and chlorpyrifos toxicity on selected soil quality parameters and ecological indicators (eco-indicators) in a fescue prairie grassland soil. Burnt soil from an area subjected to prescribed fire in 2017 and annual cattle grazing and unburnt reference soil from adjacent areas subjected to grazing alone were collected in 2021 and exposed to varying doses of chlorpyrifos. Results showed no interactive effect of fire-grazing and chlorpyrifos toxicity on the measured soil quality parameters and eco-indicators. Whereas the fire-grazing effect in the burnt soil significantly enhanced soil quality parameters like base cations and available nitrogen, it exacerbated chlorpyrifos toxicity on key soil eco-indicators like oribatid mites and soil extracellular enzyme, acid phosphatase. Based on the eco-indicator sensitivity distribution framework, the burnt soil with history of fire and grazing was generally susceptible to chlorpyrifos, with ecological hazard concentrations at 5% (HC5Eco) to 50% (HC50Eco) ranging from 0.08 to 1.5 mg/kg compared with 0.5 to 4.0 mg/kg in the unburnt soil with grazing history alone. Regardless of the fire treatment, arylamidase, an enzyme crucial for nitrogen mineralization, was the most sensitive soil eco-indicator to chlorpyrifos toxicity. These findings suggest that fire, in combination with grazing, may increase the susceptibility of soil eco-indicators to chlorpyrifos toxicity, potentially due to changes in organic matter quality or increased stress from pyric byproducts.
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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 | 0.001 | 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 | 0.000 | 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".