Land cover dynamics following a deforestation ban in northern Costa Rica
Bibliographic record
Abstract
Forest protection policies potentially reduce deforestation and re-direct agricultural expansion to already-cleared areas. Using satellite imagery, we assessed whether deforestation for conversion to pasture and cropland decreased in the lowlands of northern Costa Rica following the 1996 ban on forest clearing, despite a tripling of area under pineapple cultivation in the last decade. We observed that following the ban, mature forest loss decreased from 2.2% to 1.2% per year, and the proportion of pineapple and other export-oriented cropland derived from mature forest declined from 16.4% to 1.9%. The post-ban expansion of pineapples and other crops largely replaced pasture, exotic and native tree plantations, and secondary forests. Overall, there was a small net gain in forest cover due to a shifting mosaic of regrowth and clearing in pastures, but cropland expansion decreased reforestation rates. We conclude that forest protection efforts in northern Costa Rica have likely slowed mature forest loss and succeeded in re-directing expansion of cropland to areas outside mature forest. Our results suggest that deforestation bans may protect mature forests better than older forest regrowth and may restrict clearing for large-scale crops more effectively than clearing for pasture.
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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 | 0.000 |
| 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) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".