Tropical dry forests in Venezuela: assessing status, threats and future prospects
Bibliographic record
Abstract
SUMMARY Tropical dry forests may be among the world's most threatened ecosystems, but few studies have objectively quantified their status and threats. This study analysed Venezuelan dry forests at multiple scales, assessing status, present threats and the policy context shaping their future. Historical and current dry forest cover at both national and local scales were contrasted, and a set of quantitative risk assessment criteria applied. While dry forests were vulnerable nationally, in northern-central locations they were endangered. Clearing for cattle ranching and for intensive and subsistence agriculture were the principal factors driving dry forest loss at the national scale, while at a local level, urbanization and fire seemed to be the primary threats. The analysis emphasized the separation of risk assessment from the very different task of establishing conservation priorities; high risk areas may not necessarily be the highest priority for investment, and policy makers may become explicitly aware of the spatial scale at which their policies are implemented, as well as how these policies may affect or be affected by the status of ecosystems beyond their area of influence. The main challenge to future dry forest conservation is a paucity of explicit policies for management and use. However, scientifically-based management can support positive dry forest policies in many ways, including identifying locations and protocols for ecological restoration, maintaining seed banks, quantifying baseline conditions, and monitoring genetic diversity and other indicators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".