Responses to mechanical wounding and fire in tree species characteristic of seasonally dry tropical forest of Bolivia
Bibliographic record
Abstract
Short-term responses to stem wounding were measured over a 60-day period on six tree species found in seasonally dry tropical forest in Bolivia. Three types of wounds were inflicted to simulate mechanical bark damage and bark damage caused by low- and high-intensity fires. Extent of wood discoloration associated with wounding varied with wound type and severity, with high-intensity burns associated with the greatest amount of discoloration, low-intensity burns the least, and mechanical wounds intermediate. Two thin-barked species produced a distinct ligno suberised boundary zone in the bark earlier than thicker barked species; however, all species produced a distinct wound periderm by 60 days postwounding. The amount of wood discoloration associated with wounding appeared to be independent of the thickness of the lignosuberized boundary zone. Bark thickness provided a useful measure of species' resistance to wood discoloration with low-intensity burns but not with high-intensity burns where bark occasionally separated from the cambium or developed cracks and fissures. Variability in short-term responses to wounding and other factors may result in differences in the composition and abundance of microorganisms that colonize the wounds, with implications for reductions in wood quality and decay development.
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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".