The Duff Moisture Code and the limits of sustainable combustion: examining the evidence for a widely used threshold
Bibliographic record
Abstract
Background Canadian fire managers rely on the value of the Duff Moisture Code (DMC) for estimating lightning ignition and sustained smouldering in ground fuels. A simple rule used widely operationally suggests that lightning does not ignite fires and smouldering is not sustainable until the DMC >20. Aims We examine the strength of evidence supporting this simple rule. Methods We used daily lightning, fire and weather data from 2000 to 2019 to estimate the probability of lightning fire ignition across a number of regions in Canada. We also examined datasets of forest floor consumption from experimental burns carried out in pine forests in Canada. Key results Neither the 20 years of lightning fire ignition data nor the observed forest floor consumption data reveal consistent signals of an ignition threshold at or around DMC = 20. Conclusions and Implications Increasing DMC is associated with increasing probability of ignition from lightning and the extent of forest floor consumption, but there is little to no evidence to support the existence of a meaningful threshold across Canada when DMC is ~20. Users of this simple rule, be it for lightning ignition or fire perimeter extinction, should be aware that the data do not support a meaningful threshold around this value.
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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 | 0.013 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".