Impact of glyphosate and glyphosate‐based herbicides on the freshwater environment
Bibliographic record
Abstract
Glyphosate [N-(phosphonomethyl) glycine] is a broad spectrum, post emergent herbicide and is among the most widely used agricultural chemicals globally. Initially developed to control the growth of weed species in agriculture, this herbicide also plays an important role in both modern silviculture and domestic weed control. The creation of glyphosate tolerant crop species has significantly increased the demand and use of this herbicide and has also increased the risk of exposure to non-target species. Commercially available glyphosate-based herbicides are comprised of multiple, often proprietary, constituents, each with a unique level of toxicity. Surfactants used to increase herbicide efficacy have been identified in some studies as the chemicals responsible for toxicity of glyphosate-based herbicides to non-target species, yet they are often difficult to chemically identify. Most glyphosate-based herbicides are not approved for use in the aquatic environment; however, measurable quantities of the active ingredient and surfactants are detected in surface waters, giving them the potential to alter the physiology of aquatic organisms. Acute toxicity is highly species dependant across all taxa, with toxicity depending on the timing, magnitude, and route of exposure. The toxicity of glyphosate to amphibians has been a major focus of recent research, which has suggested increased sensitivity compared with other vertebrates due to their life history traits and reliance on both the aquatic and terrestrial environments. This review is designed to update previous reviews of glyphosate-based herbicide toxicity, with a focus on recent studies of the aquatic toxicity of this class of chemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".