Replication Data for: Deforestation and secondary growth in Costa Rica along the path of development
Bibliographic record
Abstract
Over the past 50 years, Costa Rica has experienced extensive landscape changes. It is, therefore, important to understand the historical changes that have occurred and how the forests will change in the future, which provides the objective of this study. To determine the historical deforestation trends and link them to the different biogeophysical and socioeconomic variables, forest maps from 1960 to 2013 were used in the Dinamica Environment for Geoprocessing Objects (Dinamica EGO) to create deforestation models for Costa Rica. The base model was built and analyzed for changes in landscape metrics such as patch size and distance between 1960 and 2013. After validation of the model’s ability to replicate patterns, the landscape change was then projected to 2069. The historical model validated at 85% accuracy within 600 m for both the 2005–2013 and 1997–2005 iterations. All national future projections exhibited an increase in forest area, including the most extreme deforestation scenarios. Future projections are increasingly important given changes in the global socio-political structure, climatic change, and the ever-increasing globalization of capitalistic endeavors. The trajectory of the forest in Costa Rica can also serve as a way to track both these global pressures on the natural landscape, and as a proxy for how to manage deforestation in other similar political and geographic areas of the tropics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.031 |
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".