Effects of forest conservation and management on volume growth, harvested amount of timber, carbon stock, and amount of deadwood in Finnish boreal forests under changing climate
Bibliographic record
Abstract
We employed a forest ecosystem model (SIMA) to study how the changes in forest conservation area and management affect the volume growth, harvested amount of timber, carbon stock, and amount of deadwood in Finnish boreal upland forests under current and changing climates (RCP4.5 and RCP8.5) over 2010–2099. Simulations were carried out on National Forest Inventory plots using three different forest conservation scenarios (baseline and 10% and 20% increases of conservation area) and three thinning regimes (baseline and maintenance of ±20% stocking in thinning compared with recommendations). An increase of forest conservation area increased the volume growth, carbon stock, and quantity of deadwood in forests, as did the maintenance of 20% higher stocking in thinning. Maintenance of 20% lower stocking in thinning increased, in general, the amount of harvested timber, but it could not compensate for the decrease of harvested timber due to increase of conservation area. Climate warming greatly increased all of the studied variables in northern Finland but decreased them in southern Finland, the most under the strongest climate warming scenario, RCP8.5. Climate warming also increased the quantity of deadwood throughout Finland. To conclude, we found clear trade-offs for production of different ecosystem services.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".