Integrated carbon analysis of forest management practices and wood substitution
Bibliographic record
Abstract
The complex fluxes between standing and harvested carbon stocks, and the linkage between harvested biomass and fossil fuel substitution, call for a holistic, system-wide analysis in a life-cycle perspective to evaluate the impacts of forest management and forest product use on carbon balances. We have analysed the net carbon emission under alternative forest management strategies and product uses, considering the carbon fluxes and stocks associated with tree biomass, soils, and forest products. Simulations were made using three Norway spruce ( Picea abies (L.) Karst.) forest management regimes (traditional, intensive management, and intensive fertilization), three slash management practices (no removal, removal, and removal with stumps), two forest product uses (construction material and biofuel), and two reference fossil fuels (coal and natural gas). The greatest reduction of net carbon emission occurred when the forest was fertilized, slash and stumps were harvested, wood was used as construction material, and the reference fossil fuel was coal. The lowest reduction occurred with a traditional forest management, forest residues retained on site, and harvested biomass was used as biofuel to replace natural gas. Product use had the greatest impact on net carbon emission, whereas forest management regime, reference fossil fuel, and forest residue usage as biofuel were less significant.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".