The recent fire history of the Table Mountain National Park and implications for fire management
Bibliographic record
Abstract
This paper provides an assessment of fire regimes in the Table Mountain National Park over the past four decades. We compiled a GIS database of all fires between 1970 and 2007 and analysed the fire regime in terms of the frequency, season and size of fires and the relationship between fire occurrence and fire weather. Most fires (90.5% of area burnt) occurred in summer and autumn, the ecologically acceptable season for fires. However, mean fire return intervals declined by 18.1 years, from 31.6 to 13.5 years, between the first and last decades of the record respectively. The area subjected to short (≤ six years) intervals between fires covered > 16% of the park in the last two decades of the record, compared to ~ 4% in the first two decades. A relatively small number of large fires dominated in terms of area burnt. Of the 373 fires recorded, 40 fires > 300 ha burnt 75% of the area, while 216 fires < 25 ha burnt 3.4% of the area. Fires occurred under a wide range of weather conditions, but large fires were restricted to periods of high fire danger. Prescribed burning was a relatively unimportant cause of fires, and most (> 85%) of the area burnt in wildfires. Areas subjected to short fire return intervals should be considered for management interventions. These could include the re-establishment of extirpated fire-sensitive species, the clearing of invasive alien plants and increased precautions for the prevention or rapid suppression of future accidental fires.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".