Restoration of low-intensity fire in <i>Quercus-Pinus</i> mixedwoods following a prolonged period of fire exclusion
Bibliographic record
Abstract
Low-intensity surface fire is required to restore and maintain Quercus-Pinus mixedwood composition, structure, and function. However, historical fire exclusion has resulted in altered vegetation-fuel-fire feedbacks in long-unburned mixedwoods. Fire is now being reintroduced to reduce fire-intolerant understory and midstory stem density, consume excessive litter accumulation, eradicate the duff layer, and achieve various other goals; however, the consequences of restoring fire are poorly understood. The goal of this study was to quantify the effects of fire reintroduction on intra-stand Pinus echinata neighborhoods in a mixed Quercus-P. echinata stand following an extended period of fire exclusion. We report results from a 3-year sampling of the effects of one dormant season and one early growing season fire on woody plant dynamics, understory light, fuel conditions, and P. echinata basal duff-ring accumulation. The sapling assemblage was unaffected by one fire, but the second fire resulted in shoot mortality across all taxa. Understory light availability was unchanged after two fires, and litter depth was significantly reduced after the first and second fire. We found that fire seasonality was likely more important than fuel conditions to achieve desired fire effects. Our results indicated that continued fire and midstory treatment will be required to regenerate P. echinata.
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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".