Deforestation in Costa Rica: A Quantitative Analysis Using Remote Sensing Imagery<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT Accurate estimates of forest cover and forest fragmentation are critical for developing countries such as Costa Rica, which holds four to five percent of the world's plant and bird species. We estimated forest cover for Costa Rica using Landsat 5 Thematic Mapper satellite scenes acquired between 1986 and 1991. In 1991, 29 percent (ca 14,000 km2) of the land cover of Costa Rica was closed forest cover; of that forested area, ca 30 percent is protected by national conservation policies. Forest loss in a study area representing ca 50 percent of Costa Rica's territory during a five‐year period (1986–1991) was 2250 km2, and the estimated deforestation rate was ca 450 km2/yr, or ca 4.2 percent/yr, of remaining forest cover. Forests are almost completely eliminated from the Tropical Moist Forest and Premontane Moist Forest life zones, and the level of fragmentation of remaining forests may be more advanced than previously thought. RESUMES La obtención de estimaciones precisas de la cobertura forestal y su nivel de fragmentación es crítica para la conservatión de la biodiversidad. Costa Rica contiene en su territorio nacional entre el cuatro y cinco por ciento de todas las especies de plantas y aves del mundo. Este estudio provee una estimatión precisa de la cobertura nacional forestal del país y su distribution a nivel ecológico basada en imágenes de satélite adquiridas entre 1986 y 1991 por el Trazador Temático Landsat 5. Para 1991, el país tenía una cobertura forestal de bosque perennifolio del 29 por ciento (ca 14,000 km2); el 71 por ciento de este bosque carecía de protectión mientras que el 29 por ciento restante está protegido por el Sistema Nacional de áreas de Conservación. La pérdida total de bosque en el area de estudio, durante un periodo de cinco años (1986‐1991) y la cual representa ca 50 por ciento del territorio nacional, rue de 2250 km2. La tasa de deforestatión estimada es de ca 450 km2 por año, o ca 4.2 por ciento anual. El proceso de deforestatión tropical ha eliminado casi completamente los bosques húmedo tropical y húmedo premontano del país. Los resultados de este trabajo sugieren que el nivel de fragmentación de los bosques costarricenses puede ser mas grande de lo que se habiá pensado.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".