Fire as an interactive component of dynamic vegetation models
Bibliographic record
Abstract
Fire affects ecosystems by altering both their structure and the cycling of carbon and nutrients. The emissions from fires represent an important biogeochemical pathway by which the biosphere affects climate. For climate change studies it is important to model fire as a mechanistic climate‐dependent process in dynamic global vegetation models (DGVMs) and the terrestrial ecosystem components of climate models. We expand on those current approaches which neglect disturbance by fire, which use constant specified loss rates, or which depend on simple empirical relationships, and develop a process‐based fire parameterization for use in the terrestrial ecosystem components of climate and Earth system models. The approach is straightforward and general enough to apply globally and for current and future climates. All three aspects of the fire triangle, fuel availability, the readiness of fuel to burn depending on conditions, and the presence of an ignition source, are taken into account. The approach also represents some anthropogenic effects on natural fire regimes, albeit in a simple manner. The fire parameterization is incorporated in the Canadian Terrestrial Ecosystem Model (CTEM) which simulates net primary productivity, leaf area index, and vegetation biomass. The fire parameterization generates burned area, alters vegetation biomass, and generates CO 2 emissions. The parameterization is tested by comparing simulated fire return intervals and CO 2 emissions with observation‐based estimates for tropical savanna, tropical humid forests, mediterranean, and boreal forest locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".