Short-term survival and crown rebuilding of European broadleaf tree species following a severe ice storm
Bibliographic record
Abstract
Ice storms cause widespread damage to forests in many temperate regions, leaving behind many live trees with severe crown damage. Following a severe ice storm in 2014 that damaged forests across Slovenia, we examined how tree-level attributes influenced survival and crown rebuilding three growing seasons after the storm. Field sampling was carried out in four mature stands dominated by native broadleaf species. Of the 763 sampled trees, the annual mortality rate following the storm was 2.2%, and nearly all trees that died experienced >75% crown removal. Oak (Quercus petraea (Matt.) Liebl.) and chestnut (Castanea sativa Mill.) had higher rates of mortality than beech (Fagus sylvatica L.) and maple (Acer pseudoplatanus L.). Mixed models revealed that survival significantly increased with tree diameter and decreased with increasing crown damage. Although we observed sprouting across all the dominant species, maple, oak, and chestnut showed a more vigorous response than beech, and maple had the fastest sprout growth. Model results showed that sprout density and length increased with level of crown damage. The results indicate that these broadleaf forests are resilient to severe ice damage. Consequently, hasty salvage cutting of trees with canopy damage should be avoided, as many individuals with >75% crown damage are likely to survive and recover.
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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 | 0.001 | 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".