The legacy of forest management in tropical forests: analysis of its long-term influence with ecosystem models
Bibliographic record
Abstract
Forest management can modify key ecosystem attributes, affecting tree growth long after the end of forest management. Long-term influence of current management on forest recovery has been explored with the FORECAST model in Pinus caribaea Morelet plantations in western Cuba. Management for three different products was simulated: biomass, fibre and timber, with differences in rotation length and harvest intensity. Our results show that biomass production can produce ecosystem degradation that may need centuries to recover. If fibre is the objective of management, ecosystem recovery would be faster than managing for bioenergy. However, only if timber is the final objective the ecosystem might be able to keep similar conditions to the natural forest. In conclusion, our results show that forest management legacies can be a key factor in accelerating of delaying forest ecosystem recovery, depending on the exploitation intensity. These results also show the utility of ecosystem-level management models to analyze alternative management scenarios and their effects on the forest ecosystem.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".