Early seral pathways of vegetation change following repeated short-interval, high-severity wildfire in a low-elevation, mixed conifer – hardwood forest landscape of the Klamath Mountains, California
Bibliographic record
Abstract
We compared early seral development between stands subject to single and repeated high-severity wildfire in low-elevation, mixed conifer – hardwood forests in the Klamath Mountains, California, USA. We used a before–after, control–impact (BACI) approach to assess changes in the density of conifer regeneration and the cover of multiple components of vegetation structure (conifers, hardwoods, shrubs, forbs, and graminoids) and compare pathways of seral development between plots that burned once and plots that burned twice. Fifty-three field plots were established 6 years following a high-severity fire in 2004. Nineteen of these plots experienced a second high-severity wildfire 11 years later (2015), and all plots were remeasured in 2016–2017. Conifer regeneration was abundant following the first fire but was greatly reduced by the second fire. Plots that did not reburn increased in conifer, hardwood, and shrub cover, whereas plots that reburned increased in forb cover and decreased in shrub, hardwood, and conifer cover. Despite conifer loss, we found little evidence of shifts to nonforested states following repeated fire due to resilience of resprouting hardwoods. Our results indicate that repeated high-severity fire has the potential to protract early seral development and catalyze transitions from mixed conifer – hardwood forest to hardwood-dominated early seral conditions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".