First-year impacts of shelterwood logging on understory vegetation in an old-growth pine stand in central Ontario, Canada
Bibliographic record
Abstract
There is evidence in the literature that a variety of logging practices may result in significant short and long-term changes to vegetation in the forest understory, however, these changes are still largely unknown for many forest types. The objective of this study was to determine the short-term effects of shelterwood logging on tree and non-arboreal plant species composition and diversity in both the understory and sapling strata within the Owain Lake Stand of old-growth red and eastern white pine located in Temagami, Ontario. The most significant changes were increases of white birch (800%), red maple (363%) and bracken fern (110%) in the understory. The most significant decreases within the understory occurred in mosses and liverworts (110%), Canada mayflower (49%) and starflower (28%). Bracken fern, red maple, and bush honeysuckle were the three most abundant species in the post-harvest understory plant community and will probably increase in their abundance under the present disturbed condition. A second shelterwood cut in 20 to 40 years may further facilitate an increase in these three species primarily by increasing light levels at the forest floor. All three species are very vigorous and are likely to dominate the forest understory until the upper canopy closes resulting in decreased light intensity at the forest floor.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".