Stand-specific litter moisture content calibrations for the Canadian Fine Fuel Moisture Code
Bibliographic record
Abstract
A large dataset of litter moisture measurements collected at several sites across Canada by the Canadian Forest Service over the period from 1939 to 1961 is analysed. The stands in which sampling was carried out were described by three main variables: forest type (pine, spruce, Douglas fir, mixedwood and deciduous), season (spring, summer and fall), and stand density (light, moderate and dense). All three variables were found to have a significant influence on the relationship between the Canadian Forest Fire Weather Index System’s Fine Fuel Moisture Code (FFMC) and surface litter moisture. Moisture in the upper duff layer was also found to have a significant influence on the relationship between FFMC and litter moisture content, with a wetter duff layer leading to moister surface conditions than would be indicated by the FFMC value. A model for litter moisture is developed, which provides a method of adjusting the standard FFMC value for the influences of forest type, stand density, season and duff moisture content.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".