Effects of forest management intensity and climate change severity on volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine, Norway spruce, and silver birch stands in boreal conditions
Bibliographic record
Abstract
We studied how management intensity and climate severity affect volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine ( Pinus sylvestris (L.)), Norway spruce ( Picea abies (L.) Karst.), and silver birch ( Betula pendula Roth.) dominated stands in the Republic of Karelia and Arkhangelsk region of northwest Russia. Using the forest ecosystem model (SIMA) under different climates (current and representative concentration pathway scenarios, RCP4.5 and RCP8.5), no-thinning, low, medium, and high intensity thinning rotational forestry regimes were simulated. Under RCPs, the volume growth and timber yield (5%–53%), carbon stocks (1%–22%), and deadwood amounts (11%–75%) increased for all Scots pine and silver birch stands. The use of low intensity management increased volume growth and carbon stocks (3%–16%) and deadwood amount (up to 60%) under RCPs, but not timber yield (±3%) in these stands. For Norway spruce stands, the volume growth (5%–26%), timber yield (23%–75%), and carbon stocks (5%–15%) decreased under RCP8.5, but deadwood amount increased (up to 142%). Intensive management increased volume growth (4%–19%), timber yield (4%–63%), carbon stocks (up to 14%), and deadwood amounts (up to 49%). Our results highlight that effects of climate severity and management intensity are site and species-specific for Eurasian’s boreal forests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 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".