A Conceptual Interpretation of the Drought Code of the Canadian Forest Fire Weather Index System
Bibliographic record
Abstract
The Drought Code (DC) was developed as part of the Canadian Forest Fire Weather Index System in the early 1970s to represent a deep column of soil that dries relatively slowly. Unlike most other fire danger indices or codes that operate on gravimetric moisture content and use the logarithmic drying equation to represent diffusion, the DC is based on a model that balances daily precipitation and evaporation. This conceptually simple water balance model was ultimately implemented using a “shortcut” equation that facilitated ledgering by hand but also mixed the water balance model with the abstraction equation, obscuring the logic of the model and concealing two important variables. An alternative interpretation of the DC is presented that returns the algorithm to an equivalent but conceptual form that offers several advantages: The simplicity of the underlying water balance model is retained with fewer variables, constants, and equations. Two key variables, daily depth of water storage and actual evaporation, are exposed. The English system of units is eliminated and two terms associated with precipitation are no longer needed. The reduced model does not include or depend on any soil attributes, confirming that the nature of the “DC equivalent soil” cannot be precisely known. While the “Conceptual Algorithm” presented here makes it easier to interpret and understand the logic of the DC, users may continue to use the equivalent “Implemented Algorithm” operationally if they wish.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".