Glyphosate-Based Herbicides Alter the Reproductive Morphology of Rosa acicularis (Prickly Rose)
Bibliographic record
Abstract
Glyphosate is a broad-spectrum herbicide that is widely used in many different commercial formulations. Glyphosate-based herbicides (GBH) are used in forestry operations to reduce populations of plants that compete with merchantable conifers. Past research has found that low-dose GBH applications caused male sterility in agriculturally relevant plants, sparking a need to determine the potential impacts of forestry-related GBH applications on understory plants. We investigated the effects of GBH on the reproductive morphology of Rosa acicularis , a highly prevalent understory shrub within British Columbia, Canada, growing on three operational forestry cutblocks treated with 1.782 kg a.i./ha of glyphosate, in the Omineca Region, and also in a controlled experiment. We analyzed floral and pollen morphology from treated plants and compared these with untreated plants in both scenarios. Pollen viability of treated plants was reduced by an average of 66%, and >30% of anthers were non-dehiscent compared to controls across our three field sites and experimental plants. We also found alterations in pollen and petal morphology in flowers from treated sites and glyphosate residues present in floral tissues 2 years after GBH applications. It is important to fully understand how long GBH-induced change will impact forest vegetation, to preserve natural forest biodiversity and reduce anthropogenic influences on boreal forest ecosystems.
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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".