Short-term survival and crown rebuilding of European broadleaf tree species following a severe ice storm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it