Landscape- and site-level responses of woody structure and ground flora to repeated prescribed fire in the Missouri Ozarks
Bibliographic record
Abstract
Landscape-scale prescribed burning may be appealing for natural community restoration due to operational efficiency and possible heterogeneity in fire effects across broad spatial scales. We monitored plant community responses for >15 years with variable prescribed fire frequencies applied across a Missouri Ozarks landscape stratified into distinct ecological site types. Through the study period, burning had no effect on the overstory tree density or basal area. Midstory stem densities decreased rapidly in periodically burned units and more gradually with annual fire. Burning increased total ground flora cover and the cover of forbs, grasses, and legumes. The effect of burning on species richness differed among site types, with increased species richness through time on exposed slopes. There was no effect of prescribed burning on species richness on two of three protected slope site types, and annual burning decreased species richness in upland waterways. Among the site types, the upland waterways had the most species associated with pre-burn communities. We conclude that (i) burning consistently increased cover of ground layer vegetation across the landscape, while decreasing the midstory stem densities, and (ii) site type moderated ground flora richness response, with more pronounced effects of prescribed burning on exposed sites than on protected sites.
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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".