Historical Patterns of Natural Forest Management in Costa Rica: The Good, the Bad and the Ugly
Bibliographic record
Abstract
We present an in-depth analysis of natural forest management practices in Costa Rica based on a new historical forestry GIS database encompassing five conservation regions in the country where selective logging has been prevalent for the last 18 years. Natural forest management refers to the selective logging practices for the production of timber in natural forest ecosystems. The study considers natural forest management plans developed following a standardized format, based on Forestry Law 7575, established in 1996. Our results show a positive aspect of forest management where a large percentage of the managed forest is still standing, contrary to other tropical countries where selective logging promotes land use change. The negative finding is that managed forest areas occur in the last remaining forest fragments outside of protected areas, which challenges the continuity of selective logging in the near and mid future due to the potential impoverishment of the isolated forest stands. The most negative aspect is the recent establishment, by law, of a very short return logging cycle (10–15 years—minimum cutting cycle) as it contradicts ecological studies showing that tropical forests require over 60 years for the forest structure and composition to return to a state similar to pre-logging states. Our results should inform decision makers and managers in conservation areas to review current policies and establish new benchmarks for sustainable forest management in the country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".