Reformulation of the Drought Code in the Canadian Fire Weather Index System Implemented in New Zealand
Bibliographic record
Abstract
Abstract The fire danger rating system implemented in New Zealand is the Canadian Fire Weather Index (FWI) System developed 40 years ago for Canadian temperate forests. Issues have been raised in relation to this system when applied in other regions with different climate and vegetation environments. For the first time, two methods were proposed for improving the Drought Code (DC) component of the FWI System for New Zealand. The first method (PotE) employs a potential evaporation (PE) scheme that considers wind speed, surface air stability, and water vapor mixing ratio gradient. The second method (soilM) uses soil moisture. For the latter, when soil moisture is derived from observations, the calculated DC represents the actual drought status of the soil. DC and FWI have been calculated with the original and the two new DC methods at 28 climate stations in New Zealand for a pair of 2-yr periods. The Joint U.K. Land Environment Simulator (JULES) was run to provide the PE and soil moisture for the two methods. The original DC method underestimated the drought status in New Zealand, especially in summer, leading to underestimation of FWI. The PotE method significantly overestimated the drought status in summer. The errors in the calculated drought status and FWI were largely reduced by using the soilM method with simulated soil moisture from JULES. In this paper, the reasons for this reduction in error are investigated by testing the sensitivity of DC to surface evaporation and to soil parameters. Potential benefit is found from using the proposed soilM method for monitoring drought status and for FWI calculations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".