Effects of the Herbicide Metolachlor and Pond Drying on Growth and Development of Wood Frog Tadpoles (<i>Lithobates sylvaticus</i>)
Bibliographic record
Abstract
Pesticides and climate change are both thought to contribute to the global amphibian decline, yet their combined effects are still poorly understood. Metolachlor is a widespread herbicide applied across North America, but little is known about its effects on amphibians. We used a replicated mesocosm experimental design with different levels of drying (i.e., no drying and medium and rapid drying) and metolachlor concentrations (0, 0.8, 8, and 80 µg/L) to assess their respective and combined effects on wood frog (Lithobates sylvaticus) larvae throughout metamorphosis. Metolachlor had no significant effect on survival and development of tadpoles. However, metolachlor significantly interacted with drying levels to reduce the growth of tadpoles, which was mainly due to a difference detected among metolachlor concentrations under the rapid drying treatment. Drying also directly reduced growth and body mass at metamorphosis. Our results suggest that environmental stressors, such as drying, should be considered in toxicological experiments to provide relevant exposure conditions to pesticides for ephemeral pond species in the context of global climate change. Environ Toxicol Chem 2023;42:1772-1781. © 2023 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".