Effects of Herbicides on Flowering
Bibliographic record
Abstract
Herbicides have been shown to reduce flower production and to delay flowering, with results varying among herbicides and tested plant species. We investigated the effects of herbicides on flowering in an extensive greenhouse study conducted in Canada and Denmark. The effects of low doses of 5 different herbicides (bromoxynil, ioxynil + bromoxynil, metsulfuron-methyl, clopyralid, and glyphosate), simulating realistic drift scenarios (1 and 5% recommended field rates), on plant flowering were examined using 9 wild plant species exposed at either the seedling (6- to 8-leaf) or flower bud stage. Following herbicide exposure, initial flowering date as well as flower production over time were recorded over the growing period. The effect of herbicides on cumulative flower numbers and flowering time were modeled using Gompertz growth models. Significant delays to peak flowering and/or reductions in flower production were observed in at least one plant species for all tested herbicides, with glyphosate often exhibiting the greatest negative effects, that is, plant death. Except for ioxynil + bromoxynil, there was no clear evidence of either the seedling or the flower bud stage being more sensitive. Overall, 58% of all species × life stage × herbicide treatments resulted in either a statistically significant or a strong decline in flower production with herbicide application rates up to 5% of recommended field rates, whereas significant or strong delays in peak flowering were also detected but were slightly less common. Effects at 1% label rates were minimal. Simultaneous delays to peak flowering and reductions in total flower production occurred in approximately 25% of all cases, indicating that herbicide application rates simulating realistic drift scenarios would likely have negative effects on wild floral communities. Environ Toxicol Chem 2020;39:1244-1256. © 2020 SETAC.
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) | 0.000 | 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".