Radiative forcing of forest biomass production and use under different thinning regimes and initial age structures of a Norway spruce forest landscape
Bibliographic record
Abstract
We studied how different thinning regimes and initial age structures of a Norway spruce (Picea abies (L.) Karst.) forest landscape affect the radiative forcing of forest biomass production and use. We considered the effects of forest carbon sequestration, substitution of materials and fossil fuels with forest biomass, and timber use efficiency. The initial age structures of our hypothetical forest landscapes in the middle boreal zone in Finland were young, middle-aged, and mature. Forest landscapes were thinned using either the current thinning recommendations (baseline) or maintaining 20% higher or 20% lower stocking over the 80-year study period. We employed forest ecosystem model simulations together with a life cycle assessment tool. The highest carbon sequestration was obtained by maintaining higher stocking in the landscapes. The initially middle-aged and mature age structures resulted in the strongest cooling of the climate in the first three decades of the simulation, but the highest cooling was found in the young age structure. However, radiative forcing was less sensitive to the thinning than to the substitution or timber use efficiency. Our results indicate that modeled climate impacts are affected by both initial age structure and forest management, which should be considered when generalizing the climate change mitigation potential of forests and forestry.
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 | 0.001 |
| 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) | 0.001 | 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".