Mountain pine beetle attack alters the chemistry and flammability of lodgepole pine foliage
Bibliographic record
Abstract
During periods with epidemic mountain pine beetle ( Dendroctonus ponderosae Hopkins) populations in lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) forests, large amounts of tree foliage are thought to undergo changes in moisture content and chemistry brought about by tree decline and death. However, many of the presumed changes have yet to be quantified. In this study, we quantified and compared fuel moisture, chemistry, and resulting flammability of bark beetle affected foliage in terms of ignitability, combustibility, consumability, and sustainability at a site in far eastern Idaho, USA. Results revealed substantial decreases in moisture content, the proportion of starches and sugars, and crude fat and increases in the proportions of lignin, cellulose, and hemicellulose in foliage of trees attacked in the previous year (yellow foliage) or more than two years previously (red foliage). Increases in emission rates of several terpenes that were correlated with flammability were also detected in yellow foliage. The flammability of fresh yellow and red foliage increased with regard to ignitability and sustainability, with shorter times to ignition, lower temperatures at ignition, and higher heat yields when compared with unattacked green foliage. Our results confirm the overwhelming importance of fuel moisture on flammability and suggest that fuel chemical composition also has significant effects on lodgepole pine foliage flammability.
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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) | 0.000 | 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".