Incorporating solar radiation into the litter moisture model in the Canadian Forest Fire Danger Rating System
Bibliographic record
Abstract
The Canadian Forest Fire Danger Rating System (CFFDRS) is used throughout Canada, and in a number of countries throughout the world, for estimating fire potential in wildland fuels. The standard fuel moisture models in the CFFDRS are representative of moisture in closed canopy jack pine or lodge pole pine stands. These models assume full canopy closure and do not therefore account for the influence of solar radiation and thus cannot readily be adapted to more open environments. Recent research has seen the adaptation of the CFFDRS’s hourly Fine Fuel Moisture Code (FFMC) model (which represents litter moisture) to open grasslands, through the incorporation of an explicit solar radiation term. This current study describes more recent extension of this modelling effort to forested stand situations. The development and structure of this new model is described and outputs of this new model, along with outputs from the existing FFMC model, are compared with field observations. Results show that the model tracks the diurnal variation in actual litter moisture content more accurately than the existing model for diurnal calculation of the FFMC in the CFFDRS. Practical examples of the application of this system for operational estimation of litter moisture are provided for stands of varying densities and types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".