Prescribed fire effects on oak woodland advance regeneration at the prairie–forest border in Kansas, USA
Bibliographic record
Abstract
Understanding the effects of fire on advance regeneration of oak (Quercus L.) species and their competitors is an important step in determining the role of prescribed fire in regenerating and restoring upland oak ecosystems. Our study aimed to understand how dormant-season prescribed fire affects advance regeneration of chinkapin oak (Quercus muehlenbergii Engelm.), black oak (Quercus velutina Lam.), and their major competitors at sites targeted for woodland restoration and management. We analyzed relationships between stem size and survival probability; determined the effect of fire temperature on survival probability; and compared how mortality, sprouting, and survival differed among species. For chinkapin oak, black oak, and bitternut hickory (Carya cordiformis (Wangenh.) K. Koch), mortality was low and rate of sprouting was high after shoot dieback. Initial basal diameter was significantly related to survival probability after one fire for all species except chinkapin oak and black oak. Height was a significant predictor of survival probability for all species except chinkapin oak. Although sugar maple (Acer saccharum Marsh.) also responded to fire by sprouting, it did so at a much lower rate than oaks and hickory (20% versus 43%–68%). These data reveal that heavily invaded, fire-suppressed woodlands on the prairie–forest border region may not experience major structural and compositional shifts without repeated burning or mechanical treatments.
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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".