Evaluating Alternative Weed Management Strategies for Three Montana Landscapes
Bibliographic record
Abstract
Abstract Determining the best strategy for allocating weed management resources across and between landscapes is challenging because of the uncertainties and large temporal and spatial scales involved. Ecological models of invasive plant spread and control provide a practical tool with which to evaluate alternative management strategies at landscape scales. We developed a spatially explicit model for the spread and control of spotted knapweed and leafy spurge across three Montana landscapes. The objective of the model was to determine the ecological and economic costs and benefits of alternative strategies across landscapes of varying size and stages of infestation. Our results indicate that (1) in the absence of management the area infested will continue to increase exponentially leading to a substantial cost in foregone grazing revenues; (2) even though the costs of management actions are substantial, there is a net economic benefit associated with a broad range of management strategies; (3) strategies a that prioritize targeting small new infestations consistently outperform strategies that target large established patches; and (4) inconsistent treatment and short-term delays can greatly reduce the economic and ecological benefits of management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".