A silviculture application of the glyphosate-based herbicide VisionMAX to wetlands has limited direct effects on amphibian larvae
Bibliographic record
Abstract
Herbicides are commonly used in agriculture and silviculture to reduce interspecific competition among plants and thereby enhance crop growth, quality, and volume. Internationally, glyphosate-based herbicides are the most widely used herbicides in both of these sectors. Laboratory and mesocosm studies have demonstrated that some formulations are toxic to amphibian larvae below concentrations that approximate predicted maximal or "worst-case" exposure scenarios. However, field studies have not found evidence of toxicity at these concentrations. The authors conducted a replicated field experiment involving 10 naturalized wetlands split in half with an impermeable plastic barrier to assess the direct toxicity of a glyphosate formulation commonly used in silviculture (VisionMAX™). The herbicide formulation was applied directly to the surface of one side of each wetland at one of two target aqueous exposure rates (high = 2,880, low = 550 µg acid equivalents [a.e.]/L), and the other side was left as an untreated control. The survival and growth of green frog larvae (Lithobates clamitans) were assessed for two years following herbicide treatment. The herbicide did not have a negative impact on survival or growth of L. clamitans larvae at either treatment level. In fact, mean larval abundance was typically greater in the treated sides than in control sides within the year of herbicide application. These results indicate that typical silviculture use of VisionMAX poses negligible risk to larval amphibians, likely because the combined effects of sorption and degradation in natural wetlands limit the exposure magnitude and duration.
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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".