Herbicide usage for invasive non‐native plant management in wildland areas of North America
Bibliographic record
Abstract
Summary In North America, herbicides are commonly used to control non‐native invasive plants on public wildlands. Little is known about the magnitude, efficacy and financial costs of this practice, although this information is crucial for policymakers, researchers, land managers, pesticide producers and the general public. In Canada and Mexico, herbicide usage data have not been tracked by agencies. In theUSA, data archiving has been implemented by federal land managing agencies. However, while area sprayed and amounts of herbicides have been documented to varying degrees, efficacy and financial costs have not been recorded in a standardized and consistent manner and data publication has been insufficient. Based on requested data, we estimate that in theUSA, half a million hectares of public wildlands were sprayed with herbicides in 2010, representing 201 tonnes. Although non‐selective, glyphosate was the most commonly used active ingredient. Synthesis and applications. Increasing efforts by land management agencies to collect and share herbicide usage data is a key step towards narrowing the knowledge gap on herbicide usage in invasive non‐native plant management on public wildlands. Land managers and policymakers in particular would benefit from an enhanced flow of information on efficacy, costs and effects of herbicides.
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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 | 0.001 |
| 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 | 0.000 | 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".