Simple models for predicting dead fuel moisture in eucalyptus forests
Bibliographic record
Abstract
Fire behaviour prediction requires models of dead fuel moisture that are both accurate and suitable for use for operational applications. The paper investigates two methods of developing a simple operational fine fuel moisture model from a more complex process-based model. The first simple model is a table of fuel moisture predictions for values of air temperature, relative humidity, wind speed and solar radiation. The second model reduces the original model to a single differential equation, which may be used on low-powered computers. The simple models are tested against the output of the original model and against observations from two case studies in dry eucalyptus forest in south-western Australia. The single differential equation model was capable of reproducing the prediction of the process-based model at all times of the day, with mean error (ME) in predictions of –0.1% and mean absolute error (MAE) of 0.6%. The table model performed less well, with ME = –0.7% and MAE = 1.1% at 1500 hours, and ME = –1.2% and MAE = 3.0% at other times of the day.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".