Long-term forest damage due to an extreme weather event: An ice storm mediated by elevation causes tree breakage in sub-tropical China
Bibliographic record
Abstract
Extreme weather events pose an ever-greater threat to people, infrastructure, and nature. Forest ecosystems are highly sensitive to extreme cold events that can disrupt ecosystem functions, especially in montane regions. Ice storms can be particularly destructive, with rapid ice accretion causing tree branches to break, even snapping or uprooting entire trees. In March 2022, the Shennongjia forest in central China experienced severe ice storm conditions that severely damaged over 230,300 ha. We utilized this opportunity to assess the vulnerability of different tree types (coniferous, deciduous, and evergreen broad-leaved) and stand compositions to damage resulting from ice glaze along an elevation gradient from 1,200 to 2,400 m a.s.l. Among the 7,144 trees surveyed, 10.1% suffered some extent of damage, which was most prolific in the middle elevation zone. While 96.8% of all damage occurred to deciduous broadleaved trees that dominated the forest community, the most severe damage (uprooting and lower trunk breakage) occurred to coniferous trees. The extent and severity of tree damage were moderated by forest composition, with secondary effects of forest structure and slope. Abiotic factors predominantly affected coniferous trees. We emphasize that more research and monitoring are needed to better understand the full impact of extreme weather events on forests, especially as the frequency and intensity of these events increases due to climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".