Bibliographic record
Abstract
In many regions of the world, fires are the primary environmental disturbance producing a mosaic of burned and unburned patches varying at temporal and spatial scales and providing a variety of ecosystem services. Fire perimeters mark the separation between the burned and unburned matrix of a fire. In prior studies in the United States, Australia, and Alberta, variations in the fire environment, fuel, weather, topography, and anthropogenic factors, affected fire perimeter formation. One of the critical challenges in interpreting and comparing regional variations in the fire cessation process is that each study employs a different sample distance and analysis technique. In this study, I examined fire cessation in the western Canadian Rocky Mountain region, where no fire extinguishment studies have been undertaken despite human values at risk facing increased fire hazards. This study investigates how fire environment factors influence fire cessation on the 2017 Verdant Creek Fire in Kootenay National Park. The Verdant Creek Fire is ideally suited to this research as it burned under a variety of environmental conditions, with a varying application of suppression techniques. This work evaluated the performance of 16 distances of analysis for comparing exterior unburned areas with interior burned areas to identify how static variables influence fire cessation. Two spatial and temporal scales assessed the influence of weather on fire boundary formation. The potential influence of fire suppression on fire cessation was also examined. Data were extracted using GIS and analyzed with statistical modelling using matched case-control conditional logistic regression. Predictive fire boundary models were compared to determine the effectiveness of different distances of analysis and predictor variables. Results indicated that fire boundary formation was strongly influenced by fuel composition, arrangement, and to a limited extent, topography. Weather influenced fire boundary formation, but mainly in areas where suppression occurred. Suppression was successful in periods of diminished weather conditions, and areas near waterways. The influence of vegetation was largely consistent regardless of the implementation of suppression tactics. While results from the weather model have applications in operational fire management, occurring over a limited period (1–14 days), the stable fire environment model has applications in strategic planning as it uses variables that are relatively consistent over extended periods (1–5 years). Results from the best sample distance were used to develop a Spread Potential Index (SPI). The SPI was used to map the probability of fire spread. The SPI has potential uses in strategic fire management activities as a tool for rapid visual assessment on the influence of temporally stable fire environmental factors on fire cessation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".