An assessment of the implementation and outcomes of recent changes to fire management in the Kruger National Park
Bibliographic record
Abstract
This paper reviews recent changes in fire management in the Kruger National Park, and assesses the resulting fire patterns against thresholds of potential concern. In 2002, a lightning-driven approach was replaced by an approach that combined point ignitions with unplanned and lightning fires. The approach aimed to burn an annual target area, determined by rainfall and fuel conditions, in point-ignition fires of different sizes. Most of the original fire-related thresholds of potential concern (TPCs) were incorporated into the new approach. The annual target area to be burnt ranged from 12 to 24% of the park between 2002 and 2006. The total area burnt generally exceeded the targets each year, and management fires accounted for less than half of the total area burnt. The fire regime was dominated by very large fires (> 5 000 ha) which accounted for 77% of the total area burnt. New TPCs were developed to assess whether the fire regime encompassed a sufficient degree of variability, in terms of fire intensity and the spatial distribution of burnt areas. After assessment and adjustment, it appears that these TPCs have not yet been exceeded. The point-ignition approach, and its evaluation in terms of variability and heterogeneity, is based on the untested assumption that a diverse fire regime will promote biodiversity. This assumption needs to be critically assessed. We recommend that the practice of point ignitions be continued, but that greater efforts be made to burn larger areas earlier in the season to reduce large and intense dry-season fires.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".