Regional risks of wind damage in boreal forests under changing management and climate projections
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it