Ground-layer response to group selection with legacy-tree retention in a managed northern hardwood forest
Bibliographic record
Abstract
We examined the effects of group selection with legacy-tree retention on ground-layer or understory diversity and composition in an uneven-aged northern hardwood forest in the Upper Peninsula of Michigan. We sampled 20 reference plots in the surrounding forest matrix and 49 openings with radii of 0.5 (n = 16), 0.75 (n = 17), and 1.0 (n = 16) times mean canopy tree height (22 m). Resultant opening areas were 321 ± 16 (mean ± SEs), 697 ± 21, and 1256 ± 39 m2, respectively. Each opening contained a centrally located legacy tree. Two years after harvesting, ground-layer diversity was significantly higher in openings than on reference plots (p < 0.05) because of an influx of early seral, wetland, and weedy exotic species. The importance of aggressive ruderals (i.e., Carex ormostachya Wieg. and Rubus idaeus subsp. strigosus (Michx.) Focke) increased significantly (p < 0.001) with increasing opening area. Although the importance and cover of several late-seral species were lower in openings compared with the forest matrix, few species found in the matrix were wholly absent from the openings. These results suggest that ground-layer plant communities in managed northern hardwood forests may display a high degree of resilience to intermediate-intensity disturbances.
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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 | 0.000 | 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".