Decision Analysis to Evaluate Control Strategies for Crested Wheatgrass (<i>Agropyron cristatum</i>) in Grasslands National Park of Canada
Bibliographic record
Abstract
Abstract Protected area managers often face uncertainty when managing invasive plants at the landscape scale. Crested wheatgrass, a popular forage crop in the Great Plains since the 1930s, is an aggressive invader of native grassland and a problem for land managers in protected areas where seeded roadsides and abandoned fields encroach into the native mixed-grass prairie. Given limited resources, land managers need to determine the best strategy for reducing the cover of crested wheatgrass. However, there is a high degree of uncertainty associated with the dynamics of crested wheatgrass spread and control. To compare alternative management strategies for crested wheatgrass in the face of uncertainty, we conducted a decision analysis based on information from Grasslands National Park. Our analysis involves the use of a spatially explicit model that incorporates alternative management strategies and hypotheses about crested wheatgrass spread and control dynamics. Using a decision tree and assigning probabilities to our alternative hypotheses, we calculated the expected outcome of each management alternative and ranked these alternatives. Because the probabilities assigned to alternative hypotheses are also uncertain, we conducted a sensitivity analysis of the full probability space. Our results show that under current funding levels it is always best to prioritize the early detection and control of new infestations. Monitoring the effectiveness of control is paramount to long-term success, emphasising the need for adaptive approaches to invasive plant management. This type of decision analysis approach could be applied to other invasive plants where there is a need to find management strategies that are robust to uncertainty in the current understanding of how these plants are best managed.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".