Prescribed fire effects on the herbaceous layer of mixed-oak forests
Bibliographic record
Abstract
In 1994, a multidisciplinary project was established to study the effects of prescribed fire on oak forests in southern Ohio. Here we describe the herbaceous layer response to fires over a 5-year period. In four study sites, treatments imposed were unburned, periodic (1996 and 1999), and annual (19961999) fires. Sample plots (n = 108) were stratified by an integrated moisture index. Species' frequencies were recorded annually, and a total of 452 species (97% native) were documented. Though species composition was significantly affected by fire, the effects were shown by ordination to be small in magnitude relative to overall compositional variation. Burned areas developed greater small-scale species richness as grasses, summer forbs, and seed-banking species increased in frequency; however, these changes were also not large in magnitude. Though a few species increased substantially via germination after fire, most common species exhibited frequency increases or decreases of <10% on burned units. Fire effects on vegetation were largely similar between annual and periodic burns and also among integrated moisture index classes. Direct fire effects on vegetation were limited by the dormant-season timing of burns and the resprouting of woody plants. Indirect effects were limited, as fires caused relatively minor changes in forest structure and resource availability in these long-unburned forests.
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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".