Rating the susceptibility of forests to mountain pine beetle infestations: the impact of data
Bibliographic record
Abstract
British Columbia is currently experiencing the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic on record. The spatial extent of this infestation highlights the need for large-area forest management. We explore the use of three large-area data sets for implementing a stand-scale model of forest susceptibility that quantifies the probability of loss of pine basal area because of attack by the mountain pine beetle. Using these data sets, we investigate the impact of surrogate variables, which is necessary when variables required for the susceptibility model are not present in a data set. The impact of the source data information content on the susceptibility model output is also analyzed. Results indicate that the susceptibility model is sensitive to both surrogate variables and data sources and suggest that landscape level application of the susceptibility model, which was developed using stand-scale relationships, is problematic. Of particular concern is the use of photointerpreted data sets for model parameterization. The information content in photointerpreted data sets is much different than data on similar forest characteristics collected in the field and provides an inadequate substitute for implementing the forest susceptibility model.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".