Effects of forest management and harvesting intensity on the timber supply from Finnish forests in a changing climate
Bibliographic record
Abstract
We studied the potential effects of management and harvesting intensity on the timber supply from Finnish forests in a changing climate and, consequently, the possibilities of meeting the increasing wood demand of the growing forest-based bioeconomy. The study employed data from the 11th National Forest Inventory of Finland. Plots located on forest land assigned to timber production were used to develop two even-flow harvesting scenarios with annual timber harvesting targets of 60 and 80 million m 3 . Calculations were done for a 90-year simulation period under the current and changing climates using recent-generation (Coupled Model Intercomparison Project Phase 5) global climate model projections under three representative concentration pathways forcing scenarios (RCP2.6, RCP4.5, and RCP8.5). Intensified management used improved seed and seedling stock in artificial regeneration. It also used fertilization on subxeric pine-dominated and mesic spruce-dominated stands and ditch maintenance on 40% of drained peatlands, when the growing stock characteristics fulfilled a set of predetermined criteria. Our results showed that, with intensified management, it is possible to harvest 80 million m 3 ·year −1 of timber under mild (RCP2.6) and moderate (RCP4.5) climate change without decreasing the growing stock volume at the country level during the 90-year simulation period. This is not possible under severe climate change (RCP8.5) due to the rapid decline in forest growth, particularly in the south after about 30 years.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".