Bibliographic record
Abstract
This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Fire effects are modeled for a variety of reasons including: to evaluate risk, to develop treatment prescriptions, to compare management options, and to understand ecosystems. Fire effects modeling may be conducted at a range of temporal and spatial scales. First-order fire effects are those that are the direct result of the combustion process such as plant injury and death, fuel consumption and smoke production. Modeling these effects provides an important cornerstone for models that operate at larger spatial and temporal scales. Detailed physical models of heat transfer and the combustion process under development should provide a vehicle for quantifying fire treatment and predicting fire effects. Second-order fire effects are indirect consequences of fire and other post-fire interactions such as weather. They may take place a few hours to many decades after a fire. Some important second-order fire effects are smoke dispersion, erosion, and vegetation succession. Many approaches have been used to model fire effects including empirical, mechanistic, stochastic, and combinations of all three. Selection of the appropriate model approach and scale depends on the objectives of the modeler, as well as the quality and quantity of available data. This paper is not meant to provide an exhaustive review of fire effects models. Instead, it presents a background in approaches to modeling fire effects to provide managers a basis for selecting and interpreting simulation tools.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".