Are chemical or mechanical treatments more sustainable for forest vegetation management in the context of the TRIAD?
Bibliographic record
Abstract
Chemical and mechanical forest vegetation management (FVM) treatments are analyzed and compared to assess which is the most sustainable in intensively managed plantations in the context of the TRIAD. At the biological and ecological level, herbicides have been found to have more impacts on flora and fauna compared to mechanical treatments, but the differences are of short duration. The effects of noise generated by manual or mechanical brushing on wildlife have not been investigated, however. Local application of herbicide at the base of the tree should further lower these impacts. At the social level, the general public has a negative perception of chemical treatments, while mechanical treatments are well perceived. However, in terms of worker safety, chemical treatments are less risky than manual brushing (brushsaw or chainsaw). At the economic level, herbicides globally cost less and are more effective at increasing fibre production than mechanical operations. We conclude that it is difficult to assess globally what is the most sustainable option to control competing vegetation. However, the careful use of herbicide may be the most sustainable option if the added productivity thus obtained is used to increase protected areas and ecosystem-based management, as is intended with the TRIAD concept. Key words: forest vegetation management, chemical release, mechanical release, functional zoning, plantations, ecological impacts, social impacts, economical impacts, intensive management, sustainable forestry
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".