Fire-injured ponderosa pine provide a pulsed resource for bark beetles
Bibliographic record
Abstract
Bark beetles can cause substantial mortality of trees that would otherwise survive fire injuries. Resin response of fire-injured northern Rocky Mountain ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson) and specific injuries that contribute to increased bark beetle attack susceptibility and brood production are unknown. We monitored ponderosa pine mortality and resin flow and bark beetle colonization and reproduction following a prescribed fire in Idaho and a wildfire in Montana. The level of fire-caused tree injury differed between the two sites, and the level of tree injury most susceptible to bark beetle attack and colonization also differed. Strip-attacked trees alive 3 years post-fire had lower levels of bole and crown injury than trees mass attacked and killed by bark beetles, suggesting that fire-injured trees were less well defended. Brood production of western pine beetle ( Dendroctonus brevicomis LeConte) did not differ between fire-injured and uninjured trees, although mountain pine beetle ( Dendroctonus ponderosae Hopkins) brood production was low in both tree types, potentially due to competition with faster developing bark beetle species that also colonized trees. Despite a large number of live trees remaining at both sites, bark beetle response to fire-injured trees pulsed and receded within 2 years post-fire, potentially due to a limited number of trees that could be easily colonized.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".