Comparison of forest fire suppression in Quebec and Sweden : a historical review, 1998-2015
Bibliographic record
Abstract
This study compared two suppression systems in Quebec and Sweden: a centralized wildfire agency working with remote fires in Quebec, and a decentralized fire suppression system in Sweden, with each municipality responsible for extinguishing fires in their community. Their management approaches reflect differences in population density and land area. To understand these study areas, this study collected 25 variables, from eight national databases, that describe suppression cost, area burned, and financial efficiency for fires in 1998-2015. Descriptive analysis (histograms and frequency distributions) compared the two areas, revealing that Sweden had more fires (39,146 versus 11,211), that burned less area (0.92 ha versus 115.6 ha on average), with a lower protection cost (CAD548/ fire versus CAD10,151/ fire), and better efficiency than Quebec. Excluding fires <0.1 ha, the Swedish fires cost less to extinguish per area burned (an average of CAD839/ ha, annually, versus CAD1,860/ ha) and had a lower cost per area protected (an annual average of CAD0.04/ ha versus CAD0.52/ ha). Due to remote fire transportation needs, Quebec used more aircraft, but employed fewer people per fire. Quebec typically sent four people to the fire, while Sweden typically sent six. \n \nTo understand how firefighting agencies can suppress fires effectively and efficiently, linear models statistically evaluated the effect of suppression effort (personnel, aircraft), while controlling for climate, vegetation, remoteness, and location. Multiple lognormal models were evaluated using Akaike Information Criteria. Visual inspection of residual plots confirmed homoscedasticity, linearity, and normality assumptions. Each model used 9-16 significant variables to explain the variance and likeliness of cost (F(23,1549)=3275, p<0.001, R2 = 97.96%, AIC = 14.73), area burned (F(43,975)=210.6, p<0.001, R2 = 89.85%, AIC = 2786), and efficiency (F(23,1549)=3866, p<0.001, R2 = 98.26%, AIC = 14.73). Aircraft hours contributed more to the cost than person hours (0.59% versus 0.30% increase in cost, given a one percent increase in hours worked, p<0.001). However, person hours decreased area burned more than aircraft hours (-0.66% versus -0.31% change in area burned per one percent increase in hours worked, p<0.001). With a lower cost and larger decrease in area burned, it was more efficient (less cost per area burned) to use people than aircraft (0.30% versus 0.59% increase in cost per area burned given one percentage increase of hours worked, p<0.001). A larger, fulltime crew had a bigger impact on decreasing area burned than did temporary helpers (-0.41% versus -0.31% decrease in area burned given a percentage increase of people working, p<0.01). Therefore, the best way to suppress a fire quickly, cheaply, and efficiently is for a strong, initial attack with larger, fulltime crews.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".