Deforestation in Colombian protected areas increased during post-conflict periods
Bibliographic record
Abstract
Protected areas (PAs) are a foundational and essential strategy for reducing biodiversity loss. However, many PAs around the world exist on paper only; thus, while logging and habitat conversion may be banned in these areas, illegal activities often continue to cause alarming habitat destruction. In such cases, the presence of armed conflict may ultimately prevent incursions to a greater extent than the absence of conflict. Although there are several reports of habitat destruction following cessation of conflict, there has never been a systematic and quantitative "before-and-after-conflict" analysis of a large sample of PAs and surrounding areas. Here we report the results of such a study in Colombia, using an open-access global forest change dataset. By analysing 39 PAs over three years before and after Colombia's peace agreement with the Revolutionary Armed Forces of Colombia (FARC), we found a dramatic and highly significant increase in the deforestation rate for the majority of these areas and their buffer zones. We discuss the reasons behind such findings from the Colombian case, and debate some general conservation lessons applicable to other countries undergoing post-conflict transitions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".