Management assessment of mountain pine beetle infestation in Cypress Hills, SK
Bibliographic record
Abstract
Insect epidemics such as the mountain pine beetle (MPB) outbreak have a major impact on forest dynamics. In Cypress Hills, Canada, the Forest Service Branch of the Saskatchewan Ministry of Environment aims to control as many new infested trees as possible by conducting ground-based surveys around trees infested in previous years. Given the risk posed by MPB, there is a need to evaluate how well such a control strategy performs. Therefore, the goal of this study is to assess the current detection strategy compared with competing strategies (random search and search based on model predictions via machine learning), while taking management costs into account. Our model predictions via machine learning used a generalized boosted classification tree to predict locations of new infestations from ecological and environmental variables. We then ran virtual experiments to determine control efficiency under the three detection strategies. The classification tree predicts new infested locations with great accuracy (AUC = 0.93). Using model predictions for survey locations gives the highest control efficiency for larger survey areas. Overall, the current detection strategy performs well but control could be more efficient and cost-effective by increasing the survey area, as well as adding locations given by model predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".