Tree species diversity in managed Acadian forests of Eastern Canada
Bibliographic record
Abstract
Maintaining forest diversity is an important value in long range management planning. This study was conducted in the ecologically diverse Acadian forest region in the Province of New Brunswick, Canada across 1.65 million hectares of publicly owned (Crown) and privately owned (Freehold) land. Tree species diversity using Hill numbers was evaluated across 21 forest type/age class combinations (groups) using 1691 sample plots to assess tree species richness (0D), typical species (1D), and abundant species (2D). Across the entire study area there were 0D = 31.0 total tree species observed, 1D = 11.5 typical species, and 2D = 7.0 abundant species. Among the 21 forest types/age class combinations, the Hill numbers ranged from 0D = 16.0–28.3, 1D = 5.6–11.5, and 2D = 3.5–8.4. A comparison of public and private land ownerships showed minor differences in tree species diversity at the landscape level. More intensively managed forest types (e.g., planted stands and naturally regenerated stands with silvicultural interventions) had similar levels of landscape-scale tree species diversity as comparable forest stands receiving no silvicultural interventions. This suggests that current management practices are maintaining tree species diversity across the landscape and highlights the importance of tailored management regimes for different forest types to support this diversity.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".