Parks, People and Pixels: Evaluating Landscape Effects of an East African National Park on its Surroundings
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
Landscapes surrounding protected areas, while still containing considerable biodiversity, have rapidly growing human populations and associated agricultural development in most of the developing world that tend to isolate them, potentially reducing their conservation value. Using field studies and multi-temporal Landsat imagery, we examine a forest park, Kibale National Park in western Uganda, its changes over time, and related land cover change in the surrounding landscape. We find Kibale has successfully defended its borders and prevents within-park deforestation and other land incursions, and has maintained tree cover throughout the time period of the study. Outside the park there was a significant increase in tea plantations and continued forest fragmentation and wetland loss. The question of whether the park is a conservation success because of the network of forest fragments and wetlands or in spite of them remains unanswered.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it