Satellite-based comparison of fire intensity and smoke plumes from prescribed fires and wildfires in south-eastern Australia
Bibliographic record
Abstract
Smoke pollution from wildfires can adversely affect human health, and there is uncertainty about the amount of smoke pollution caused by prescribed v. wildfires, a problem demanding a landscape perspective given that air quality monitoring is sparse outside of urban airsheds. The primary objective was to assess differences in fire intensity and smoke plume area between prescribed fires and wildfires around Melbourne and Sydney, Australia. We matched thermal anomaly satellite data to databases of fires in forests surrounding both cities. For each matched fire we determined hotspot count and quantified their intensity using the fire radiative power (FRP) measurement. Smoke plumes were mapped using MODIS true colour images. Wildfires had more extreme fire intensity values than did prescribed burns and the mean size of wildfire plumes was six times greater than of prescribed fire plumes for both cities. Statistical modelling showed that the horizontal area covered by smoke plumes could be predicted by hotspot count and sum of FRP, with differences between cities and fire type. Smoke plumes from both fire types reached both urban areas, and particulate pollution was higher on days affected by smoke plumes. Our results suggested that prescribed fires produced smaller smoke plume areas than did wildfires in two different flammable landscapes. Smoke plume and FRP data, combined with air pollution data from static monitors, can be used to improve smoke management for human health.
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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 | 0.001 |
| 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".