Temporal patterns of ground flora response to fire in thinned <i>Pinus</i>–<i>Quercus</i> stands
Bibliographic record
Abstract
The ground flora stratum affects stand structure, resource acquisition, nutrient cycling, and taxonomic richness in forest ecosystems. Disturbances such as thinning and prescribed fire alter forest understory growing conditions that generally increase ground flora cover and richness. We studied annual changes in ground flora assemblages over three growing seasons after fire in thinned and frequently burned (3-year rotation) Pinus–Quercus stands. Our results corroborated trends from other studies that indicated greater ground flora richness and cover after thinning and burning compared with thin-only treatments. We also found that the stratum experienced relatively rapid succession between growing seasons that complimented the tolerance succession model. Forbs had reduced cover and richness from increasingly difficult growing conditions over time and were replaced by woody plants, shrubs, and seedlings. This likely occurred from changing competition dynamics that favored quick growth in the first growing season and long-term investment in vertical growth in the third growing season. The successful regeneration pathways also fit ground flora regeneration models and added a unique pathway to strengthen the predictive power of these models. As many stand management goals are focused towards improving biodiversity, prescribed fire and thinning may be used to increase understory richness in Pinus–Quercus stands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".