Using Infrared Imagery to Assess Fire Behaviour in a Mulched Fuel Bed in Black Spruce Forests
Bibliographic record
Abstract
An experimental fire was conducted in one-year-old mulched (masticated) boreal fuels, where all aboveground biomass was mulched with no stems removed or left standing. Typical mulching practices remove remnant biomass; leaving biomass in situ reduces overall management input. While fuel quantities were not explicitly reduced, availability of fuels to fire was reduced. Infrared imagery was obtained to quantify rate of spread and intensity to a 1 m resolution. In-stand totalizing heat flux sensors allowed for the observation of energy release near the surface. When compared with the pre-treatment fuel-type M-2 (mixedwood, 50% conifer), rates of spread were reduced 87% from an expected 8 m min−1 to observed values 1.2 m min−1. Intensity was also reduced from 5000 kWm−1 to 650kWm−1 on average. Intermittent gusts caused surges of fire intensity upwards of 5000 kW m−1 as captured by the infrared imagery. With reference to a logging slash fuel type, observed spread rates declined by 87% and intensity 98%. Independent observations of energy release rates from the radiometers showed similar declines. As mulching is a prevalent fuel management technique in Alberta, Canada, future studies will contribute to the development of a fire behaviour prediction model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".