Replication Data for: Climate change scenarios and projected impacts for the forest productivity in the Guanacaste province: lessons for tropical forest regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Guanacaste Province of Costa Rica is home to highly diverse forests which are under threat of degradation due to ongoing climatic changes. There is concern that increasing temperatures and changes in precipitation will force these forests outside of their optimal growth ranges leading to degradation, measured using forest productivity. The objectives of this study are, therefore, to project and assess climatic changes in Guanacaste and the to build a relationship between these climatic changes and forest productivity with the goal of projecting productivity trends into the future. The ClimateSA model was used to project the RCP 4.5 and 8.5 scenarios from 2018 until 2080 and then assess these projections for the mean and extreme future conditions. Furthermore, the MODIS Gross Primary Productivity (GPP) algorithm was used to build a relationship between forest productivity and the Vapour Pressure Deficit scalar (VPD scalar) and project GPP alteration under future climatic scenarios both seasonally and annually. Results indicate that Guanacaste’s mean annual precipitation will stay within the historic levels for both the RCP 4.5 and 8.5 scenarios. The monthly and annual temperatures increase in all projections. Results also indicate that the productivity-climate relationship follows a quadratic relationship between GPP and the VPD scalar. This quadratic relationship leads to areas with higher precipitation (high VPD scalar) experiencing an increase in GPP as they dry in the future. In drier areas (low VPD scalar), reduced precipitation will stabilize or decrease GPP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it