Characterisation of the fuel and fire environment in southern Ontario’s tallgrass prairie
Bibliographic record
Abstract
Prescribed burning can be an integral part of tallgrass prairie restoration and management. Understanding fire behaviour in this fuel is critical to conducting safe and effective prescribed burns. Our goal was to quantify important physical characteristics of southern Ontario’s tallgrass fuel complex prior to and during prescribed burns and synthesise our findings into useful applications for the prescribed fire community. We found that the average fuel load in tallgrass communities was 0.70 kg m–2. Fuel loads varied from 0.38 to 0.96 kg m–2. Average heat of combustion did not vary by species and was 17 334 kJ kg–1. A moisture content model was developed for fully cured, matted field grass, which was found to successfully predict moisture content of the surface layers of cured tallgrass in spring. We observed 25 head fires in spring-season prescribed burns with spread rates ranging from 4 to 55 m min–1. Flame front residence time averaged 27 s, varying significantly with fuel load but not fire spread rate. A grassland spread rate model from Australia showed the closest agreement with observed spread rates. These results provide prescribed-burn practitioners in Ontario better information to plan and deliver successful burns.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".