Bibliographic record
Abstract
Several methods are available for estimating the moisture content of 10-h response time fuels in the U.S. National Fire Danger Rating System (NFDRS). These fuels are represented by an array of four 1.27 cm diameter ponderosa pine (Pinus ponderosa Dougl. ex Laws.) dowels weighing about 100 g when oven dry. The prediction model currently used in the NFDRS is driven by information from afternoon weather readings. To improve responsiveness of the predictions to weather change, a 10-h stick moisture content prediction model is developed that uses observations (air temperature and relative humidity, insolation, and rainfall amount) available from a remote automatic weather station (RAWS). Equations describing the transfer of heat and moisture at the surface and within a 10-h stick are derived and then solved numerically. Collection of field experimental data on weather, stick weight, and stick temperature to guide development of the model is briefly described, and predicted and observed mean moisture contents are compared. Additional 10-h stick moisture content data, collected independently, are used to test model predictions. Calculated values are sometimes outside the bounds of variability in moisture content determined from the data, suggesting the need for further tests. The model simulates diurnal change in moisture content and temperature of 10-h sticks but can be adapted to cylindrical wood sticks of any practical size.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".