Conservation strategies for montane cloud forests in Costa Rica: the case of protected areas, payments for environmental services, and ecotourism
Bibliographic record
Abstract
During the 1970s and 1980s, a series of technical reports predicted that, based on the rate of deforestation taking place in Costa Rica at the time, most of the country's forest cover would disappear before the end of the century. The first response to counteract this undesirable situation was the creation of a system of National Parks. These protected areas now constitute the main repositories of the remaining cloud forest of Costa Rica. However, not all cloud forest is protected by the National Park System; other areas are protected by private reserves whose main income is ecotourism. Recently, Costa Rica introduced a novel system for the payment of environmental services (PES) provided by forests as compensation to forest owners for conserving their forests instead of converting them to economically more profitable land uses. The PES system acknowledges the following services of forest ecosystems: (i) greenhouse gas effect mitigation via carbon sequestration, (ii) water resources protection for urban, rural, or hydro-electric uses, (iii) biodiversity protection, and (iv) scenic value. This chapter describes the potential and current distribution of cloud forest in Costa Rica in relation to National Protected Areas, Private Reserves, and the PES system. A brief description of these and other financial mechanisms to support conservation of cloud forest is also presented. […]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".