Prescribed fire effects on understory woody plants and fuels in <i>Quercus</i>–<i>Pinus</i> mixedwoods
Bibliographic record
Abstract
To enhance forest resilience to predicted increases in forest stressors, managers increasingly desire ecologically based restoration approaches that increase ecosystem adaptation potential. Mixedwood stands, which contain a range of life history and functional traits, may be more resistant and resilient to ecosystem stressors. Management of Quercus–Pinus mixedwood stands includes the use of prescribed fire, which requires an understanding of vegetation-fuels-fire feedbacks in these ecosystems. However, a paucity of knowledge exists on the intra-stand spatial patterns of fire effects. We analyzed the effects of a newly initiated prescribed fire program on the intra-stand characteristics of understory woody plants and fuelbed composition and loading in a long-unburned Quercus –Pinus mixedwood stand in Tennessee, USA. We sampled vegetation and fuels in two plots, one that experienced two prescribed fires (burned plot), and one fire-excluded plot directly adjacent (unburned plot). On the burned plot, we recorded lower sapling and seedling densities across taxa. Spatial analysis of advance reproduction in the burned plot indicated a combination of patchy fire effects, canopy openings, and high-light understory environments. We documented significant reductions in total fuel mass. The combination of spatial analysis and ordination revealed that prescribed fires homogenized fuel loads within the burned plot.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".