Preliminary assessment of the impact of long-term fire treatments on <i>in situ</i> soil hydrology in the Kruger National Park
Bibliographic record
Abstract
There has been significant attention focused on the impacts of fire frequency and season of burn on ecological processes in the Kruger National Park (KNP). Whilst there has been some examination of these fire effects on soil properties, the explicit linkages of these effects to the hydrology of soils in burnt areas has remained a gap in our understanding. During August 2010, a field scoping campaign was undertaken to assess the impacts, if any, of long-term fire treatments on the hydrology of soils on the experimental burn plots (EBPs) in the KNP. Using various hydrometric and soil physical characterisation instruments soil, hydraulic conductivity and soil strength variations were determined across the extreme fire treatment on the EBPs, the annual August (high fire frequency) plots and the control (no burn) plots, on both the granite and basalt geologies of Pretoriuskop and Satara, respectively. It was found that there were soil hydrological and structural differences to fire treatments on the basalt burn plots, but that these were not as clear on the granite burn plots. In particular, hot, frequent fires appeared to reduce the variation in soil hydraulic conductivity on the annual burn plots on the basalts and led to reduced cohesive soil strength at the surface.Conservation implications: The KNP burn plots are one of the longest running and well studied fire experiments on African savannahs. However, the impacts of fire management on hydrological processes in these water-limited ecosystems remains a gap in our understanding and needs to be considered within the context of climate and land-use changes in the savannah biome.
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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 | 0.000 |
| 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) | 0.001 | 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 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".