Post-fire surface fuel dynamics in California forests across three burn severity classes
Bibliographic record
Abstract
Forest wildfires consume fuel and are followed by post-fire fuel accumulation. This study examines post-fire surface fuel dynamics over 9 years across a wide range of conditions characteristic of California fires in dry conifer and hardwood forests. We estimated post-fire surface fuel loadings (Mg ha−1) from 191 repeatedly measured United States national inventory plots in dry conifer and hardwood stands of 49 California forest wildfires and identified differences across fire severity classes – low, moderate and high. No significant change in duff load was detected within the first 9 years post-fire across all forest types and fire severities. Litter, 1-h and 10-h fuels exhibited a quadratic trend over time in dry conifer stands, peaking ~6 years after fire, whereas hardwood stands displayed a constant rate of increase in those fuel types. For 100- and 1000-h fuels, the annual rate of change was constant for dry conifer and hardwood stands with differing rates of change across fire severity classes. This study was based on an extensive, spatially balanced sample across burned dry conifer and hardwood forests of California. Therefore, the estimated patterns of fuel accumulation are generally applicable to wildfires within this population.
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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 | 0.001 | 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".