Measuring foliar moisture content with a moisture analyzer
Bibliographic record
Abstract
Near-instantaneous estimation of the moisture content of live fuels is complicated because of the large control exerted by physiological mechanisms. The commonly accepted reference method for measuring fuel moisture content is oven drying, which is time consuming. This study evaluates the use of a moisture analyzer (ML-50, A&D Company, Limited, Tokyo, Japan) for measuring the foliar moisture content of two common European species. The moisture of live leaves of Arbutus unedo L. (strawberry tree) and Quercus robur L. (pedunculate oak) was measured within a period of 15 min using two drying temperatures and compared with the oven-dried value. Correction factors were determined for estimating the oven-dried moisture content based on the measurement by the moisture analyzer. The power delivered during the drying process plays an important role in the moisture measured by the analyzer in relation to the oven-dried value. Increasing the drying time beyond the minimum period necessary for obtaining a reliable prediction of the oven-dried moisture does not significantly change the moisture measured at lower temperatures. The moisture analyzer is able to estimate the live foliage moisture content with high accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".