Study of Fuel-Smoke Dynamics in a Prescribed Fire of Boreal Black Spruce Forest through Field-Deployable Micro Sensor Systems
Bibliographic record
Abstract
Understanding the combustion dynamics of fuels, and the generation and propagation of smoke in a wildland fire, can inform short-range and long-range pollutant transport models, and help address and mitigate air quality concerns in communities. Smoldering smoke can cause health issues in nearby valley bottoms, and can create hazardous road conditions due to low-visibility. We studied near-field smoke dynamics in a prescribed fire of 3.4 hectares of land in a boreal black spruce forest in central Alberta. Smoke generated from the fire was monitored through a network of five field-deployable micro sensor systems. Sensors were placed within 500–1000 m of the fire area at various angles in downwind. Smoke generated from flaming and smoldering combustions showed distinct characteristics. The propagation rates of flaming and smoldering smoke, based on the fine particulate (PM2.5) component, were 0.8 and 0.2 m/s, respectively. The flaming smoke was characterized by sharp rise of PM2.5 in air with concentrations of up to 940 µg/m3, followed by an exponential decay with a half-life of ~10 min. Smoldering combustion related smoke contributed to PM2.5 concentrations above 1000 µg/m3 with slower decay half-life of ~18 min. PM2.5 emissions from the burn area during flaming and smoldering phases, integrated over the combustion duration of 2.5 h, were ~15 and ~16 kilograms, respectively, as estimated by our mass balance 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 | 0.000 | 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".