Foliar morphology and chemistry of upland oaks, red maple, and sassafras seedlings in response to single and repeated prescribed fires
Bibliographic record
Abstract
Managers increasingly use prescribed fire in oak forests to decrease fire-sensitive species, increase understory light, and improve oak ( Quercus spp.) regeneration. To better understand woody seedling response to burning, single and repeated (3×) prescribed fires were implemented over 6 years (2002–2007) in eastern Kentucky, and leaf traits of red ( Erythrobalanus spp.) and white oaks ( Leucobalanus spp.) were compared with competitors red maple ( Acer rubrum L.) and sassafras ( Sassafras albidum (Nutt.) Nees). Burned seedlings had higher total leaf area (TLA) because of two to three times higher TLA of sassafras. Leaf mass per area (LMA) and leaf N content per area (Narea) increased postfire but were independent of seedling identity. Canopy openness during 2006, which was lower on unburned sites (4%–8%) compared with those burned 1× (4%–16%) and 3× (7%–33%), was positively correlated with sassafras TLA, oak and sassafras Narea, and LMA of all seedling groups the subsequent year. In 2007, TLA, LMA, and Narea were positively correlated with basal diameter of all groups but most significantly for sassafras and red maple. These findings indicate that low-intensity, early growing season prescribed fire can alter seedling leaf characteristics, but not in a manner that enhances oak seedling leaf traits relative to their competitors red maple and sassafras.
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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) | 0.001 | 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".