The Risks Associated with Glyphosate-Based Herbicide Use in Planted Forests
Bibliographic record
Abstract
Glyphosate-based herbicides are the dominant products used internationally for control of vegetation in planted forests. Few international, scientific syntheses on glyphosate, specific to its use in planted forests, are publically available. We provide an international overview of the current use of glyphosate-based herbicides in planted forests and the associated risks. Glyphosate is used infrequently in planted forests and at rates not exceeding 4 kg ha1. It is used within legal label recommendations and applied by trained applicators. While the highest risk of human exposure to glyphosate is during manual operational application, when applied according to label recommendations the risk of exposure to levels that exceed accepted toxicity standards is low. A review of the literature on the direct and indirect risks of operationally applied glyphosate-based herbicides indicated no significant adverse effects to terrestrial and aquatic fauna. While additional research in some areas is required, such as the use of glyphosate-based products in forests outside of North America, and the potential indirect effects of glyphosate stored in sediments, most of the priority questions have been addressed by scientific investigations. Based on the extensive available scientific evidence we conclude that glyphosate-based herbicides, as typically employed in planted forest management, do not pose a significant risk to humans and the terrestrial and aquatic environments.
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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".