Factors affecting post-fire regeneration after coppicing of cork oak (<i>Quercus suber</i>) trees in northeastern Algeria
Bibliographic record
Abstract
Wildfires may have serious and long-lasting impacts in Mediterranean Basin oak forests. Although cork oak (Quercus suber L.) is usually considered a highly fire-resistant tree species, post-fire recovery is not guaranteed. We assessed the mortality and regeneration of 729 cork oak trees that burned and were cut in six sites with the objective of identifying the main factors affecting the short- to medium-term post-fire recovery. We used linear mixed models to investigate potential relationships among tree stump status at the end of the second and fifth growing seasons and several tree- and site-level variables. Overall tree mortality in the second year was 44% and was negatively affected by bark thickness and positively affected by root exposure, trunk injuries, and diameter, in decreasing order of importance. Between the second and fifth years, mortality increased to 53%, but this increase was only observed in sites with high grazing pressure. Grazing also had a strong negative impact on the growth of basal resprouts, thereby compromising tree recovery. The results give useful information on the need for application of adequate forest management practices (particularly avoiding debarking injuries, soil erosion, and grazing pressure) to increase the resilience of these ecosystems and reduce the negative economic and ecological impacts of wildfires.
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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 | 0.001 | 0.001 |
| 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".