Using Naturalness for Assessing the Impact of Forestry and Protection on the Quality of Ecosystems in Life Cycle Assessment
Bibliographic record
Abstract
A novel approach is proposed to evaluate the impact of forestry on ecosystem quality in life cycle assessment (LCA) combining a naturalness assessment model with a species richness relationship. The approach is applied to a case study evaluating different forest management strategies involving concomitantly silvicultural scenarios (plantation only, careful logging only or the current mix of both) combined with an increasing share of protected area for wood production in a Québec black spruce forest. The naturalness index is useful to compare forest management scenarios and can help evaluate conservation needs considering the type of management foreseen for wood production. The results indicate that it is preferable to intensify forest management over a small proportion of the forest territory while ensuring strict protection over the remaining portion, compared to extensive forest management over most of the forested area. To explore naturalness introduction in LCA, a provisory curve relating the naturalness index (NI) with the potential disappeared fraction of species (PDF) was developed using species richness data from the literature. LCA impact scores in PDF for producing 1 m3 of wood might lead to consistent results with the naturalness index but the uncertainty is high while the window leading to consistent results is narrow.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".