Testing a process-based fine fuel moisture model in two forest types
Bibliographic record
Abstract
We test the ability of a recently developed process-based fine fuel moisture model to predict the surface and profile moisture content of litter fuels in two types of eucalytpus forest in Western Australia. The model predicts fuel moisture by modelling the energy and water budgets of the litter, intercepted precipitation, and the air spaces in the litter. The model equations are solved using an initial observation of fuel moisture and boundary conditions derived from basic weather observations. Model predictions are compared with twice-daily field observations made from October 1983 to March 1984. A novel two-stage method is used to assess model performance; the ability of the model to predict whether fuel is flammable is first assessed using contingency table analysis, and then the accuracy of predictions when the fuel is flammable is assessed. The model is capable of predicting the flammability of litter in both forest types with 80%–90% accuracy. Predictions of moisture content in flammable fuels are, on average, accurate to within 3% once the model has been calibrated against field observations. The model can be adapted to other forest types by specifying suitable parameters or by calibration against field observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".