Land-cover change in the Kruger to Canyons Biosphere Reserve (1993– 2006): A first step towards creating a conservation plan for the subregion
Bibliographic record
Abstract
This paper is a first step towards a conservation plan for the Kruger to Canyons Biosphere Reserve (K2C) on the South African Central Lowveld, quantifying the historical land-cover trends (1993 - 2006). During the analysis period, 36% of the biosphere reserve (BR) underwent land-cover change. Settlement areas increased by 39.7%, mainly in rural areas, becoming denser, particularly along roadways. Human-Impacted Vegetation increased by 6.8% and Intact Vegetation declined by 7.3%, predominantly around settlement areas, which is testament to the interdependency between rural communities and the local environment. However, settlement expansion exceeded the rate of rangeland growth; in the long term, this may raise questions for sustainable resource extraction. Similarly, the block losses of intact vegetation are of concern; issues of fragmentation arise, with knock-on effects for ecosystem functioning. In the economic sector, agriculture increased by 51.9%, while forestry and mining declined by 7.1% and 6.3%, respectively. The future of these three sectors may also have significant repercussions for land-cover change in the BR. The identification of historical drivers, along with the chance that existing trends may continue, will have important implications for biodiversity protection in this landscape. Applied within a conservation-planning framework, these land-cover data, together with economic and biodiversity data, will help reconcile the spatial requirements of socio-economic development with those of conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".