Do Fires in Savannas Consume Woody Biomass? A Comment on Approaches to Modeling Savanna Dynamics
Bibliographic record
Abstract
Savanna ecosystems have long been fertile ground for mathematical modeling of vegetation structure and the role of resources and disturbance in tree-grass coexistence. In recent years, several authors have presented models that explore how savanna fires suppress the woody community, alter ecosystem dynamics, and promote grass persistence. We argue, however, that the assumption that fires influence savanna dynamics by consuming woody biomass may be wrong because, in reality, fires kill seedlings and saplings that constitute little biomass relative to adult trees. We present a simple alternative that separates the woody community into a subadult (fire-sensitive) class and an adult (fire-resistant) class and explore how this ecologically more realistic, but still simplified, model may provide better simulations of demographic processes and response to fires in savannas.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".