Examining fuel treatment longevity through experimental and simulated surface fire behaviour: a maritime pine case study
Bibliographic record
Abstract
The adequate prediction of fire behaviour characteristics for both scientific and management objectives is deeply impacted by the performance of fire behaviour models. Both the lack of experimentation and limitations in fire modelling constrain current understanding of fuel treatment effectiveness and longevity. The residual effect of a 10-year-old prescribed fire was quantified by both simulating fire behaviour and observing real-world fire behaviour in treated (T10) and untreated (U25) fuels in a 25-year-old maritime pine ( Pinus pinaster Aiton) stand in Portugal. Fire behaviour characteristics were measured in experimental surface fires (n = 36). Surface-fire behaviour was simulated using BehavePlus with custom fuel models for T10, U25, and U15 (the untreated fuel complex when the stand was 15 years old). The T10 fuel complex had significantly less decomposing litter load and shrub cover and load than the U25 fuel complex. The observed rate of fire spread did not differ between fuel complexes after accounting for the effects of other environmental variables, but flame length in T10 was 25% lower than that in U25. BehavePlus simulations contradicted the difference observed in flame length. Inconsistent and misleading assessments of fuel treatment effectiveness with detrimental impacts on the outcomes of fuel management may result from the generalized practice of solely using simulation in lieu of experimental fires.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".