Evaluation of new methods for drought estimation in the Canadian Forest Fire Danger Rating System
Bibliographic record
Abstract
Background Canadian fire management agencies track drought conditions using the Drought Code (DC) in the Canadian Forest Fire Danger Rating System. The DC represents deep organic layer moisture. Aims To determine if electronic soil moisture probes and land surface model estimates of soil moisture content can be used to supplement and/or improve our understanding of drought in fire danger rating. Methods We carried out field studies in the provinces of Alberta and Ontario. We installed in situ soil moisture probes at two different depths in seven forest plots, from the surface through the organic layers, and in some cases into the mineral soil. Results Our results indicated that the simple DC model predicted the moisture content of the deeper organic layers (10–18 cm depths) well, even compared with the more sophisticated land surface model. Conclusions Electronic moisture probes can be used to supplement the DC. Land surface model estimates of moisture content consistently underpredicted organic layer moisture content. Implications Calibration and validation of the land surface model to organic soils in addition to mineral soils is necessary for future use in fire danger prediction.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".