Modelling chamise fuel moisture content across California: a machine learning approach
Bibliographic record
Abstract
Live fuel moisture content plays a significant and complex role in wildfire propagation. However, insitu historical and near real-time live fuel moisture measurements are temporally and spatially sparse within wildfire-prone regions. Routine bi-weekly sampling intervals are sometimes exceeded if the weather is unfavourable and/or field personnel are unavailable. To fill these spatial and temporal gaps, we have developed a daily gridded chamise (Adenostoma fasciculatum) live fuel moisture product that can be used, in conjunction with other predictors, to assess current and historical wildfire danger/behaviour. Chamise observations for 52 new- and 41 old-growth California sites from the National Fuel Moisture Database were statistically related to dynamically downscaled high-resolution weather predictors using a random forest machine learning model. This model captures reasonably well the temporal and spatial variability of chamise live fuel moisture content within California. Compared with observations, model-predicted live fuel moisture values have an overall R2, root mean squared error (RMSE) and bias of 0.79, 15.34% and 0.26%, respectively, for new growth and 0.63, 8.81% and 0.11% for old growth. Given the success of the model, we have begun to use it to produce daily forecasts of chamise live fuel moisture content for California utilities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".