Bibliographic record
Abstract
The Canadian Forest Fire Danger Rating System (CFFDRS) is the cornerstone of contemporary fire management in Canada. Although the System is conceptually robust there are known issues, primarily based on limitations that existed over the last 75 years of its development. One area with many known questions is the Drought Code (DC). Within fire operations management the DC is primarily used to indicate when potential suppression issues may occur, due to burning in deeper, denser fuels that typically take longer to dry but which, once dry, can sustain deep and prolonged burning. The DC is also used to carry-over drought from one fire season to the next, when drought conditions are present. Unlike the other moisture codes in the Fire Weather Index System (a sub-system of the CFFDRS), there is much ambiguity around the DC, what it represents, how it should be interpreted and validated, and how and when it should be used to carry the accumulated moisture deficit from one fire season into the next. This latter question is more relevant than ever as we enter into an era where the effects of climate change are becoming apparent. The central goal of this thesis is to clarify some of these common operational questions through the analysis of historic fire and weather data, and new field observed moisture data. The study area included select regions within the provinces of Alberta and Ontario. Results from the thesis identify some of the sources of bias in the DC values and provide recommendations to reduce that bias. It also tested and validated new methods of soil moisture monitoring, including electronic moisture probes and land surface models. In doing so, best practices were identified that can be readily incorporated operationally to reduce errors. Where shortcomings in the science were found that limit operational use, recommendations were made to identify potential solutions needed to move the science forward. These findings will help ensure that these technologies will eventually be available for regular use in fire management and the findings herein will improve our understanding of drought and how to better represent it in the Next-Generation CFFDRS.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".