Regional risks of wind damage in boreal forests under changing management and climate projections
Bibliographic record
Abstract
We employed simulations by forest ecosystem (SIMA) and mechanistic wind damage (HWIND) models in upland boreal forests throughout Finland to study regional risks of wind damage under changing management preferences and climates (current and RCP4.5 and RCP8.5 scenarios) over 2010–2099. We used a critical wind speed for the uprooting of trees as a measure of vulnerability, which together with the probability of such wind speed defined a level of risk. Based on that, we also predicted the stem volume of growing stock at risk and the amount of damage. In this work, medium fertility sites were planted to one of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), or silver birch (Betula pendula Roth) or to the tree species that was dominant before the final clear-felling. The vulnerability to wind damage, the volume of growing stock at risk, and the amount of damage all increased, increasing the most in the south when the proportion of Norway spruce (with shallow rooting) of the growing stock increased. Under a severe climate warming, the proportion of Norway spruce decreased the most in the south, opposite to that of birch. This decreased the risk of damage in autumn (when birch is leafless), unlike in summer. The low risk of damage in the north was due to the large proportion of Scots pine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".