Linking construction timber carbon storage with land use and forestry management practices
Bibliographic record
Abstract
Abstract Consequential life cycle assessment was applied to forestry systems to evaluate the environmental balance of expanding forestry onto marginal agricultural land to supply more timber for the built environment, accounting for land use effects and product substitution. Forestry expansion to supply timber buildings could mitigate UK greenhouse gas (GHG) emissions by 2.4 Gg CO 2 eq. per ha of forest over 100 years, though net mitigation could be halved if beef production were displaced to Brazil. Forest thinning increases wood yields and percentage conversion of harvested wood to construction sawnwood, resulting in 5% greater net GHG mitigation compared with unthinned systems. Optimising the environmental sustainability of construction timber value chains in a circular, bio-based economy will require holistic accounting of land use (change), forestry management and complex flows of wood.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".