The effects of competition and herbicide drift on non-target plant populations
Bibliographic record
Abstract
Herbicide drift is the movement of herbicide away from its intended target.The effect of drift on non-target plants is considered in environmental risk assessments, where the goal of the assessment is to protect plant populations and communities.The aim of this study was to evaluate the assumption that the single species tests used in risk assessments are fully protecting wild plant populations, as they do not account for interspecific interactions.In a greenhouse two-species competition experiment, it was found that the competitive interactions between the model species, Centaurea cyanus and Silene noctiflora, were affected by low doses of glyphosate representing drift.These changes could affect both of their populations in the long-term, and would not be detected using current test guidelines.As interspecific competition is an important determinant of plant community structure, competitive interactions may need to be included in risk assessment to make more credible predictions on the effects of herbicide drift on non-target plants.My greatest achievement to date is accomplished with the submission of this thesis.Yet, it would not have been possible without many others.First and foremost, I need to extend my gratitude to my supervisor, Dr. Céline Boutin, for her continued support and enthusiasm throughout these past two years.I have been extremely fortunate to be of her grad students, benefitting from her knowledge and constructive criticism.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".