The effect of illicit crops on forest cover in Colombia
Bibliographic record
Abstract
ABSTRACTMany armed conflicts worldwide occur in biodiversity hotspots and nearly 50% of those conflicts occur in forested regions. In Colombia, the armed conflict has implied the clearing of large forest tracts for the establishment of illicit crops. The aim of this study was to assess the role that illicit crops played in the deforestation dynamics in Colombia between 2001 and 2014. We established a database with the annual deforestation rates and nine predictors for 1120 municipalities and built fixed effects models that take spatial autocorrelation into account. Model selection with AIC suggested that the area cultivated with coca crops was the best predictor of annual rates of deforestation, whereas coca crop removal was associated with increasing forest cover.According to our results, coca crops promoted deforestation in Colombia between 2001 and 2014 through indirect (spilling-over to nearby areas), immediate and temporally-lagged mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".