Forest Fire Suppression Effectiveness in the Canadian Boreal Forest
Bibliographic record
Abstract
Wildland fire management agencies engage in fire suppression operations to mitigate the potential disastrous impacts from wildland fires to communities and values. The goal of fire suppression is to reduce fireline intensity and/or limit fire growth, and ultimately extinguish the fire using resources ranging from FireRanger crews with pumps and hose, to aerial suppression, including helicopters and fixed wing aircraft that can drop large amounts of water. Deciding on the right resources to send to any individual fire is challenging as fire managers must consider the expected fire behaviour and consequent requirements for fire containment and extinguishment, along with the expected future demands for limited suppression resources throughout the day. Currently, much of this decision making is based on decades-old heuristics as well as individual fire manager’s experience. The goal of this research was to provide evidence-based information that would assist fire managers in assessing fire suppression effectiveness across a range of spatial and temporal scales, ranging from the flame to landscape scale, and from minutes to full fire seasons. Extensive datasets of fire behaviour, fire weather, fire suppression activity, and infrared (IR) detection of fire radiative power were used to study the effectiveness of water applied to spreading fire at various scales. Replicated field-based experimental burning monitored by IR imaging technology characterized the effect of water delivered at ground level on combustion zone energy change and the duration of its effect at the flame scale. At the fireline scale, I used 14 years of suppression outcomes from Air Attack Officers in Ontario to characterize the impact of water drops from airtankers on spreading fires, and I showed that the current fire intensity and resource effectiveness heuristics used across Canada are in need of modification. At the landscape scale, I characterized the factors considered by fire managers in dispatching airtankers to support FireRanger crews on the ground, and developed models which assessed the probability of suppression success on those fires that receive airtanker support. The outcomes of this thesis provide an evidence-based foundation for understanding suppression effectiveness in the boreal forest which will enhance fire manager’s ability to make informed decisions regarding fire suppression resource use and allocation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".