Patterns of flammability of the California oaks: the role of leaf traits
Bibliographic record
Abstract
Fire is one of the most important processes driving plant community composition and structure. Fire regimes are largely governed by climate, vegetation structure, and individual plant traits that influence flammability. We assessed the mechanistic drivers of flammability for a diverse group of 18 California Quercus and allied Chrysolepis and Notholithocarpus species, addressing variation in leaf physical traits, growth form (tree or shrub), phylogeny (Quercus subgenera), and fire regime (low, mixed, or high severity). Differences in flammability were not strongly driven by leaf habit, leaf margin type, or surface area to volume ratio; simple measures of leaf size accounted for most of the observed variation. Further, leaf size was tightly linked to fuelbed depth, a known driver of fire behavior. Litter from trees was generally more flammable than litter from shrubs, primarily a function of differences in leaf size. A hierarchical clustering analysis on the flammability data set divided the oaks into three clusters of low, intermediate, and high flammability, corresponding closely to high-, mixed-, and low-severity fire regimes, respectively. The link between plant flammability traits and fire regime provides further evidence that individual species affect ecosystem processes.
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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".