Role of burning season on initial understory vegetation response to prescribed fire in a mixed conifer forest
Bibliographic record
Abstract
Although the majority of fires in the western United States historically occurred during the late summer or early fall when fuels were dry and plants were dormant or nearly so, early-season prescribed burns are often ignited when fuels are still moist and plants are actively growing. The purpose of this study was to determine if burn season influences postfire vegetation recovery. Replicated early-season burn, late-season burn, and unburned control units were established in a mixed conifer forest, and understory vegetation was evaluated before and after treatment. Vegetation generally recovered rapidly after prescribed burning. However, late-season burns resulted in a temporary but significant drop in cover and a decline in species richness at the 1 m 2 scale in the following year. For two of the several taxa that were negatively affected by burning, the reduction in frequency was greater after late-season than early-season burns. Early-season burns may have moderated the effect of fire by consuming less fuel and lessening the amount of soil heating. Our results suggest that, when burned under high fuel loading conditions, many plant species respond more strongly to differences in fire intensity and severity than to timing of the burn relative to stage of plant growth.
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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 | 0.001 |
| 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) | 0.001 | 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".