Examining the utility of the Canadian Forest Fire Weather Index System in boreal peatlands
Bibliographic record
Abstract
The Duff Moisture Code (DMC) and Drought Code (DC) components of the Canadian Forest Fire Weather Index (FWI) System are used by fire managers to assess the vulnerability of organic soils to ignition and depth of burn despite being developed for upland soils. Given the need to assess wildfire risk in peatlands, we compared the DMC and DC in eight peatlands located in five regions in boreal Canada with water table position (WT) and surface volumetric moisture content (VMC). The slope of the change in WT and DC relationship ranged greatly (–0.01 to –0.11 cm) between sites and years likely due to differences in site-specific peat properties, catchment water supply, and presence of seasonal ice. A DC of 400, which has been associated with wildfire vulnerability in uplands, corresponded to a seasonal drop in WT in the range of 4–36 cm. The slopes of the relationships between DMC and DC with 5 and 15 cm VMC also varied greatly between sites. Our findings suggest that these FWI components are suitable for predicting the general moisture status and fire danger in boreal peatlands. However, there is a need for a modified DC for specific peat types to indicate when the WT has reached a critical depth upon which fire danger increases. We also present a suggested framework for the development of a new peat moisture code within the FWI.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".