Evaluating Alternative Weed Management Strategies for Three Montana Landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it