Relationships between stand growth and structural diversity in spruce-dominated forests in New Brunswick, Canada
Bibliographic record
Abstract
Relationships between stand growth and structural diversity were examined in spruce-dominated forests in New Brunswick, Canada. Net growth, survivor growth, mortality, and recruitment represented stand growth, and tree species, size, and height diversity indices were used to describe structural diversity. Mixed-effects second-order polynomial regressions were employed for statistical analysis. Results showed stand structural diversity had a significant positive effect on net growth and survivor growth by volume but not on mortality and recruitment. Among the tested diversity indices, the integrated diversity of tree species and height contributed most to stand net growth and survivor growth. Structural diversity showed increasing trends throughout the developmental stages from young, immature, mature, and overmature forest stands. This relationship between stand growth and structural diversity may be due to stands featuring high structural diversity that enhances niche complementarities of resource use because trees exist within different horizontal and vertical layers, and strong competition resulted from size differences among trees. It is recommended to include effects of species and structural diversity in forest growth modeling initiatives. Moreover, uneven-aged stand management in conjunction with selective or partial cutting to maintain high structural diversity is also recommended to maintain biodiversity and rapid growth in spruce-dominated forests.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".