Effects of atrazine and S-metolachlor on stream periphyton taxonomic and fatty acid composition
Bibliographic record
Abstract
Abstract Extensive pesticide use for agriculture diffusely pollutes aquatic ecosystems through leaching and runoff events and has the potential to negatively affect non-target organisms. Atrazine and S-metolachlor are two widely used herbicides often detected in high concentrations in rivers that drain nearby agricultural lands. To determine the effects of these two herbicides on river ecosystems, we conducted a 14-day laboratory experiment to expose river periphyton to a wide range of atrazine and S-metolachlor concentrations. The response of periphyton was evaluated using ecologically relevant endpoints including chlorophyll a fluorescence and fatty acids composition. Results showed that atrazine disrupted photoautotroph biomass measured by chlorophyll a fluorescence. Both herbicides caused dissimilarities in fatty acid profiles between control and high exposure concentrations, but S-metolachlor had a stronger effect than atrazine on the observe increase or reduction in saturated fatty acids (SFAs) and very long chain fatty acids (VLCFAs). Our study demonstrates that two commonly used herbicides, atrazine and S-metolachlor, can negatively affect the composition and fatty acid profiles of stream periphyton, thereby altering the nutritional quality of this resource for primary consumers.
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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".