Modifying the Canadian Fine Fuel Moisture Code for masticated surface fuels
Bibliographic record
Abstract
Mechanical mastication is a fuel management technique that disrupts the vertical continuity of forest fuels by shredding of trees and understory vegetation into a highly compacted surface fuel bed. Despite the increasing application of mastication to manage wildfire risk, there is little information to date on fuel moisture in masticated fuels and optimal ignition patterns for prescribed burning. We investigated the applicability of the Fine Fuel Moisture Code (FFMC), a component of the Canadian Fire Weather Index (FWI) System, in tracking the diurnal and day-to-day changes in masticated surface fuel moisture, and developed a calibration of the standard conversion between moisture content and FFMC via regression modelling. We also proposed several modifications to the FFMC model (including a solar radiation driven fuel temperature) to better estimate the fuel-specific parameters of small diameter (<1 cm) masticated surface fuels. Model validation was performed using destructive moisture content observations from a mastication treatment in west-central Alberta, Canada. A calibrated form of the moisture content to FFMC conversion produced mean error of –2.3% moisture content, and closely resembled previous FWI System calibrations for fast drying surface fuels. Our modified FFMC-based model fit well with field observations, and was capable of producing mean error of 1.0% moisture content. The fast drying that we observed highlights the need to better understand moisture dynamics of masticated fuel beds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".