Effects of forest plantation management on herbaceous-layer composition and diversity
Bibliographic record
Abstract
I compared the species composition and diversity of vascular plants in the herbaceous layer from a chronosequence of intensively managed spruce (Picea) plantations in three age-classes (57, 1012, 1416 years) with natural, mature stands (ca. 90 years) in southeastern New Brunswick, Canada. Total species richness (stand level) averaged 8184 species in the three plantation age-classes compared with 64 species in the natural stands; richness of forest habitat species alone was 3336 in the plantations and 37 in the natural stands. More fertile sites had significantly higher values for Hill's diversity indices (N0, N1, N2). Mean N0, N1, and N2(subplot level) did not differ among stand types for all species, but N1and N2were significantly greater in the natural stands than in one or two plantation age-classes for forest habitat species alone. The two younger age-classes of plantations differed significantly in composition from the natural stands and the plantations became slightly more similar (Sørensen's index) to the natural stands with increasing age. One forest habitat species was lost and 24 others decreased in cover in the plantations. Tracking of plantations over a longer time period will be needed to determine whether these forest habitat species eventually regain their former abundance.Key words: chronosequence, forest harvesting, herbaceous layer, plantations, species diversity, species composition.
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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".