A synthesis of plant invasion control: important factors to consider when choosing a control method
Bibliographic record
Abstract
Experimental studies on invasive plant control often involve a small number of invasive plants and are conducted in a particular habitat. Moreover, different invasive plant control strategies vary among studies. Therefore, it is difficult to identify the best control method when considering the individual studies separately. Here, we conduct a systematic review to find a general pattern about the most widely used measures for controlling invasive plants, based on their growth habits and invaded habitats, and explore how different control strategies vary across different experimental studies. This information may guide us to improve our control efficiency. We found that herbicide application was the most preferred method for controlling invasive plants, regardless of their growth habit and habitat type. On the other hand, the selection of an appropriate non-chemical control method depended on the growth habit and habitat of the invasive plant. In the majority of experimental studies, control treatments were applied once, and the responses of invasive plants were monitored over one growing season. Based on these results, we discuss the merits and demerits of the most widely used control methods and provide recommendations for deciding on an effective control method.
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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 | 0.049 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".