Integrating forest fuels and land cover data for improved modelling of fire containment in Albert, Canada
Bibliographic record
Abstract
<!--!introduction!--> Knowledge of pre-fire fuel type, varied by foliar moisture content, is crucial information for fire-containment detection, which is highly needed by fire management. However, the detection is still a big challenge for boreal forests in Alberta, Canada, containing a broad range of fuel types that will be either very fire-prone or fire-resistant, or in between. The primary fuel data source is the Canadian Forest Fire Behavior Prediction (FBP) system, which is used nationwide and provides a classification system of various specific fuel types yearly. Given its 100-meter resolution, it is always wondering if another fuel data based on Landsat (LSAT) in 30-meter higher resolution updated every five years poses potential in playing the amending role or not, but still needs to be determined. In this study, 241 historic fire perimeters documented during ten years (2008-2018) were examined to investigate the role of land cover attributes on the formation of fire perimeters in the Boreal Forest Region of Alberta covering 11 natural ecological sub-regions and all fire seasons, by using these two sets of fuel data respectively. Results show that the difference between the Landsat and FBP data does not affect the concordance level of the model efficiency of Matched case-control conditional logistic regression as much as regrouping the fire under different conditions including seasonality and initial fire weather. However, higher resolution of remote sensing data shows higher capability in discerning the role of fuels on fires for specific fuel types with different foliar moisture content status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".