Impacts of climate change, weather extremes and alternative strategies in managed forests
Bibliographic record
Abstract
The growth rate of most tree species in boreal forests will increase with changing climate. This increase is counterbalanced by an increased risk of damage due to extreme weather events. It is believed that the risk of storm damage will increase over time, especially if forests continue to be managed as they are today. In this study, a new landscape-level hybrid forest growth model 3PG-Heureka was developed and simulations were performed to predict the damage caused by storm events in Kronoberg county, over a period of 91 years (2010–2100) with different alternative management regimes under various climatic scenarios (historic, RCP4.5 and RCP8.5). The results indicate that damage caused by storm events could drastically reduce the annual volume increment and annual net revenue obtained from forest landscapes if current forest management regimes are used. These problems can be reduced by adopting alternative management strategies involving avoiding thinning, shorter rotation periods and planting alternative tree species. Alternative management strategies could potentially improve annual volume increments and net revenue obtained while reducing storm-felling. Planting Scots pine instead of Norway spruce across the landscape to minimize storm damage is predicted to be less effective than reducing rotation periods.
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.
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 | 0.001 |
| 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 | 0.001 |
| 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".