Perception of wildfire behaviour and fire suppression tactics among Swedish incident commanders
Bibliographic record
Abstract
Unlike most regions with high-intensity wildfire potential, Sweden lacks specialized wildfire suppression organization. Instead, wildfire suppression is handled by highly decentralized and multitask municipal rescue services. This prompts the question how the incident commanders (ICs) perceive and interpret variation in fire behaviour and how they respond to wildfire incidents with regard to dispatching for initial attack and selecting tactics. To elucidate this, we exposed a spectrum of Swedish ICs to a questionnaire and round-table-exercises of different fire scenarios. The informants had on average 13 years of experience as incident commanders and had on average managed 6 wildfires over the last 5 years. Despite minimal formal wildfire training the respondents showed reasonable consensus in rating of fire behaviour in response to fuels and weather, suggesting that their knowledge was built on personal and group experience. Likewise, they gave estimates on rate of production of hose-lays similar to published expert assessments from Canada. When exposed to a spectrum of fire scenarios, resource dimensioning by ICs was linearly related to the Canadian FWI-index, although most organizations did not have any preordained schemas or rules of initial dispatching resources to guide them. Tactics employed were based mainly on accessing the fire from the nearest road and using direct attack with hose-line laid from the engine and water ferried on trucks. In a scenario where initial attack failed, suppression crews typically fell back on roads, which however would be breached by intense fire, and which also exposed the operation to risk of being outflanked. This response was in fact similar to that employed during a 2014 catastrophic wildfire in central Sweden and may indicate a fundamental flaw in tactics employed for large and intense fires. The present structure of the Swedish wildfire suppression system developed during the second half of the 1900s and depends on rapid access to the fire by a relatively small number of firefighters. The study suggests a relatively high capacity for suppressing forest fires, despite that the organization is primarily rigged for other purposes and that ICs have minimal formal training in this area. Climate change-scenarios suggest longer fire season and more risk days in parts of the country, but the future wildfire scene may be even more sensitive to de-population and diminishing economic resources in heavily forested regions of the country.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".