Determinants of inter-annual variation in the area burnt in a semiarid African savanna
Bibliographic record
Abstract
Fire is a key driver of savannas. It was predicted that the area of a summer-rainfall savanna burnt per annum should depend on the amount of fuel, which depends on variable grass production resulting from variable rainfall, ‘carry-over’ from the preceding season and attrition of fuel by herbivores. Most fires occur during the dry season, thus the amount of green growth resulting from occasional winter rainfall could influence fuel combustibility and therefore the area burnt. These predictions were examined with a 37-year (1963–99) data set for Mkuzi Game Reserve, South Africa. Total area burnt was related to wet season rainfall separately for years with a ‘wet’ dry season or a ‘dry’ dry season. Against prediction, the amount of dry-season rainfall had no influence on the total area burnt. For years with a ‘dry’ dry season, rainfall of the preceding wet season had an additional influence. Herbivore density had no influence. A dry-season burn was twice as large as a wet-season burn, and the largest burns were the most intense. Monitoring of wet season rainfall is sufficient for planning burning programs. Intense, large fires can be achieved for control of bush encroachment following 2 or more successive years of high rainfall.
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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 | 0.001 |
| 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".