Assessing wildland fire suppression effectiveness with infrared imaging on experimental fires
Bibliographic record
Abstract
Background Suppression effectiveness is often evaluated by measuring the extent to which it slows fire spread and reduces fireline intensity. Although studies have used infrared (IR) imaging methods to explore suppression effectiveness, most do not measure or assess the influence of water application on energy release. Aims This preliminary analysis uses IR imagery to quantify the impact of suppression on fire behaviour and the reduction in energy released from a flaming fire. Methods We conducted a series of small-scale experimental burns representative of pine and grass surface litter in the Canadian boreal forest and suppressed these fires while actively monitoring fire behaviour with overhead IR imagery. We used detailed measurements of fire radiative power to estimate fire radiative energy density, forward rate of spread and fireline intensity. Key results We observed changes in fire behaviour due to suppression, quantified the duration of those reductions and detected a suppression signal through an analysis of radiative energy during the flaming combustion phase. Conclusions IR methodology is able to capture the changes in energy released from a fire due to known aspects of water application. Implications Our findings can inform methodologies for field studies on suppression effectiveness, where ground sampling techniques are impractical but airborne IR methods can be employed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".