Infrared characterization of fine-scale variability in behavior of boreal forest fires
Bibliographic record
Abstract
Spatial and temporal variability in forest fire behavior, caused by differences in microsites, fuel types and condition, topography, and other factors across even relatively small areas, has been poorly characterized in most previous studies. Past characterization of forest fires has often been limited by monitoring techniques that relied on timing systems in coarse-resolution sampling grids. We report documentation and analysis of fire behavior for several experimental fires using a camcorder-sized infrared camera mounted in a helicopter hovering over the target fires. These fires were conducted as part of the Russian FIRE BEAR Project in boreal Pinus sylvestris L. forests of central Siberia. Final results provide quantitative information on fire front location, rates of spread, temperatures, and total radiation energy (kW/m2) observed during the fires at resolutions from 2.5 to 1.0 m across experimental burn plots ranging from 2.3 to 4.0 ha. Further postfire analysis using GIS produced a detailed spatial and temporal quantification of fireline intensity (kW/m) across the plot area. This type of infrared monitoring and analysis helps to support clearer assessment of relationships between fire behavior and ecological impacts. Such data permit accurate fire behavior estimates at various temporal and spatial scales rather than using an overall plot average. This method allows the sample size to be quite large, so that statistical analysis of the fire behavior data can provide an associated level of confidence.
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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 | 0.001 |
| 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".