Recovery of late-seral vascular plants in a chronosequence of post-clearcut
Bibliographic record
Abstract
We investigated the impacts of clearcutting on the ground vegetation of remnant late-successional coastal Aca-dian forests in southwestern Nova Scotia. Vegetation was sampled in 750 1-m2 quadrats established in 16 stands belonging to different recovery periods since clearcutting (3–54 years) and 9 late-successional forests (100–165 years) with no signs of significant human disturbance. Our objectives were to: i) describe the changes in species richness, diversity, and abundance of ground vegetation after clearcutting; ii) examine the responses of residual species (i.e., late-successional flora) to clearcutting; and iii) determine whether any forest species were restricted to or dependent upon the late-successional stages of stand development for maximal frequency and/or abundance. Although clearcutting had no immediate impact on overall alpha richness or diversity, the richness and diversity of residual plants declined after canopy removal and showed no evidence of recovery over 54 years of secondary succession. Consequently, compositional differences between secondary and late-seral stands persisted for many decades after clearcutting. Several understory herbs (e.g., Coptis trifolia (L.) , Oxalis montana (L.), Monotropa uniflora (L.)) were restricted to or attained their highest frequency and abundance in late-seral forests. These results suggest that the preservation of remnant old stands may be necessary for the maintenance of some re-sidual plants in highly disturbed and fragmented forest landscapes in eastern Canada.
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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".