Forest fuel treatments in western North America: Merging silviculture and fire management
Bibliographic record
Abstract
For many years silviculture and fire management have mostly been separate forestry disciplines with disparate objectives and activities. However, in order to accomplish complex and multiple management objectives related to forest structure, fuels, and fire disturbance, these two disciplines must be effectively integrated in science and practice. We have linked scientific and management tools to develop an analytical approach that allows resource managers to quantify and evaluate the effectiveness of alternative fuel treatments in dry interior forests of western North America. The principal tool is the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) for characterizing fuel succession and fire behaviour, and for quantifying and visualizing stand structure. FFE-FVS provides a user-friendly framework that facilitates rapid evaluation of thinning and surface fuel treatments intended to reduce crown fire potential and fireline intensity. This approach quantifies fire hazard at small and large spatial scales, assists with treatment priorities and schedules, and generates stand and landscape visualizations that facilitate decisions about appropriate fuel treatments. Key words: fire behaviour, fire hazard, fuel treatments, silviculture
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 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 | 0.001 | 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".