Tree mortality patterns following prescribed fires in a mixed conifer forest
Bibliographic record
Abstract
During the late fall of 2002 we administered three burns in mixed conifer forest sites in the north-central Sierra Nevada. Eight months later we measured fire-induced injury and mortality in 1300 trees. Using logistic regression, an array of crown scorch, stem damage, fuels, and fire-behavior variables were examined for their influence on tree mortality. In Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), white fir (Abies concolor (Gord. & Glend.) Lindl.), and incense cedar (Calocedrus decurrens (Torr.) Florin), smaller trees with greater total crown damage had higher mortality rates. Smaller stem diameters and denser canopies predicted mortality best in ponderosa pine (Pinus ponderosa Dougl. ex P. Laws. & C. Laws). Duff consumption and bark char severity increased model discrimination for white fir and incense cedar and California black oak (Quercus kelloggii Newberry), respectively. In tanoak (Lithocarpus densiflorus (Hook. & Arn.) Rehd.), greater total crown damage in shorter trees resulted in higher mortality rates. Along with tree diameter and consumption of large (>7.6 cm diameter at breast height, DBH) rotten downed woody debris, fire intensity was a significant predictor of overall tree mortality for all species. Mortality patterns for white fir in relation to crown damage were similar among sites, while those for incense cedar were not, which suggests that species in replicated sites responded differently to similar burns. Our results demonstrate actual fire-behavior data incorporated into mortality models, and can be used to design prescribed burns for targeted reduction of tree density in mixed conifer forests.
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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 | 0.000 | 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) | 0.000 | 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".