Measuring duff moisture content in the field using a portable meter sensitive to dielectric permittivity*
Bibliographic record
Abstract
Duff water content is an important consideration for fire managers when determining favourable timing for prescribed fire ignition. The duff consumption during burning depends largely on the duff water content at the time of ignition. A portable duff moisture meter was developed for real-time water content measurements of non-homogenous material such as forest duff. Using circuitry developed from time and frequency domain reflectometry (TDR and FDR) technologies, this sensor measures a change in frequency that is responsive to the dielectric permittivity of the duff material placed in a sample chamber and compressed. Duff samples from four forest cover types—Douglas fir, larch, lodgepole pine and spruce/alpine fir—were used to calibrate the frequency output to volumetric water content. A second-order polynomial (R2 = 0.97) provides the best fit of the data to volumetric water content. The accuracy of the duff moisture meter is ±1.5% at 30% volumetric water content and ±4% at 60% volumetric water content. The volumetric water content can readily be converted to gravimetric water content, which is used more frequently by fire managers and as an input to predictive models of duff consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".