The influence of prefire tree growth and crown condition on postfire mortality of sugar pine following prescribed fire in Sequoia National Park
Bibliographic record
Abstract
Tree mortality is a vital component of forest management in the context of prescribed fires; however, few studies have examined the effect of prefire tree health on postfire mortality. This is especially relevant for sugar pine (Pinus lambertiana Douglas), a species experiencing population declines due to a suite of anthropogenic factors. Using data from an old-growth mixed-conifer forest in Sequoia National Park, we evaluated the effects of fire, tree size, prefire radial growth, and crown condition on postfire mortality. Models based only on tree size and measures of fire damage were compared with models that included tree size, fire damage, and prefire tree health (e.g., measures of prefire tree radial growth or crown condition). Immediately following the fire, the inclusion of different metrics of prefire tree health produced variable improvements over the models that included only tree size and measures of fire damage, as models that included measures of crown condition performed better than fire-only models, but models that included measures of prefire radial growth did not perform better. However, 5 years following the fire, sugar pine mortality was best predicted by models that included measures of both fire damage and prefire tree health, specifically, diameter at breast height (DBH, 1.37 m), crown scorch, 30-year mean growth, and the number of sharp declines in growth over a 30-year period. This suggests that factors that influence prefire tree health (e.g., drought, competition, pathogens, etc.) may partially determine postfire mortality, especially when accounting for delayed mortality following fire.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".