Are vegetation—environment relationships different between herbaceous and woody groundcover plants in barrens with shallow soils?
Bibliographic record
Abstract
The extent to which woody vegetation exhibits more expansive community structures and different relationships with environmental variables than herbaceous plants is poorly understood in savannas and barrens worldwide, especially those with shallow soils. We explored this question in oak barrens, which are savanna habitats characterized by shallow soils, in southern Ohio, USA. Groundcover plant aerial cover and environmental data were collected using 75 randomly located 1-m2 quadrats in 3 barrens. A combination of non-metric multidimensional scaling (NMDS) and β-flexible cluster analysis revealed 3 distinct herbaceous plant assemblages that varied in abundances of C4 and C3 graminoid species and several forbs. Herbaceous vegetation patterns were correlated with soil acidity, soil depth, plant available water, and especially soil organic matter and quadrat slope. By contrast, woody plants were more widely distributed than herbs, and woody vegetation patterns were strongly correlated only with soil organic matter, although they were weakly associated with quadrat slope and an index of tree influence. The expansion of woody plants into barrens is likely not restricted by most edaphic factors; thus, barrens are highly susceptible to woody plant encroachment that has been fostered by anthropogenic alterations to natural fire regimes (suppression, dormant season fires). We hypothesize that fires that are intended to mimic natural lightning fires during the growing season will be most effective in deterring woody plant encroachment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".