Assessing the future susceptibility of mountain pine beetle (<i>Dendroctonus ponderosae</i>) in the Great Lakes Region using forest composition and structural attributes
Bibliographic record
Abstract
The potential expansion of mountain pine beetle (Dendroctonus ponderosae Hopkins) from western North America into the Great Lakes Region (Michigan, Minnesota, Wisconsin, and Ontario) could negatively impact eastern pine forests. Currently, no metrics exist to assess susceptibility in the region. I have developed a hazard rating system for the Great Lakes Region that utilizes common attributes of forest structure and composition and have assessed the current susceptibility using the Forest Inventory and Analysis database. The vast majority of plots (∼90%) that contained at least one living pine species were classified as moderately or highly susceptible. Plots on federal (USDA Forest Service) lands had higher susceptibility ratings than those on private or state-owned lands. Ordination results highlighted differences among the susceptibility scores (high, moderate, and low) across plots. Plots with high susceptibility were associated with greater total plot density and pine density, and plots with low susceptibility were associated with lower total plot density and greater overstory species richness. There are still many unknowns regarding mountain pine beetle in the Great Lakes Region; however, as natural resource managers plan for the future, they may want to consider the potential arrival of mountain pine beetle in eastern pine forests when developing silvicultural prescriptions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".