Can Payments for Watershed Services Help Finance Biodiversity Conservation? A Spatial Analysis of Highland Guatemala
Bibliographic record
Abstract
ABSTRACT Payments for environmental services (PES) are a promising mechanism for conservation. PES could either provide additional funding for protected areas, pay land users to conserve biodiversity outside protected areas, or both. PES require a secure long-term source of financing to work effectively. Obtaining payments directly for biodiversity conservation is difficult, however. In most cases, water users are the most likely source, either directly or indirectly. Thus the potential for PES to help conserve biodiversity depends, in a large measure, on the degree to which areas of interest for conservation of water services overlap with areas of interest for conservation of biodiversity. This paper examines the extent of such an overlap in the case of highland Guatemala. The results show that this potential varies substantially within the country, with some biodiversity conservation priority areas having very good potential for receiving payments, and others little or none. Overall, about a quarter of all biodiversity conservation priority areas have potential for receiving payments. Thus PES are far from being a silver bullet for biodiversity conservation, but they can make a meaningful contribution to this objective.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".