Predicting postfire Douglas-fir beetle attacks and tree mortality in the northern Rocky Mountains
Bibliographic record
Abstract
Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) were monitored for 4 years following three wildfires. Logistic regression analyses were used to develop models predicting the probability of attack by Douglas-fir beetle ( Dendroctonus pseudotsugae Hopkins, 1905) and the probability of Douglas-fir mortality within 4 years following fire. Percent crown volume scorched (crown scorch), cambium injury, diameter at breast height (DBH), and stand density index for Douglas-fir were most important for predicting Douglas-fir beetle attacks. A nonlinear relationship between crown scorch and cambium injury was observed, suggesting that beetles did not preferentially attack trees with both maximum crown scorch and cambium injury, but rather at some intermediate level. Beetles were attracted to trees with high levels of crown scorch, but not cambium injury, 1 and 2 years following fire. Crown scorch, cambium injury, DBH, and presence/absence of beetle attack were the most important variables for predicting postfire Douglas-fir mortality. As DBH increased, the predicted probability of mortality decreased for unattacked trees but increased for attacked trees. Field sampling suggested that ocular estimates of bark char may not be a reliable predictor of cambium injury. Our results emphasize the important role of Douglas-fir beetle in tree mortality patterns following fire, and the models offer improved prediction of Douglas-fir mortality for use in areas with or without Douglas-fir beetle populations.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".