Stand- and tree-level responses to a range of initial basal area densities following partial harvest of complex spruce–fir stands in central British Columbia: 25-year results of a long-term field experiment
Bibliographic record
Abstract
As forest managers increasingly seek to implement partial-harvest silviculture systems in complex forests, more data are required on the potential development of the residual stand following treatment. In this study, we present 25-year data from a long-term silvicultural study of tree- and stand-level responses to differing levels of basal area following partial harvest in a complex spruce–fir forest in central British Columbia, Canada. Reducing stand basal area elicited stronger basal area increment response in fir than spruce; however, spruce productivity was overall higher than fir across all treatments. Smaller trees increased their size proportionally more than larger trees, and thinning resulted in increased recruitment of smaller trees into larger diameter classes. At the stand-level, basal area reductions to well below 20 m2/ha resulted in higher volume and basal area growth over the length of this study, and suggest that optimum stand-level growth may be achieved at lower basal areas than suggested in prior literature. We also found a positive relationship between stand-level residual basal area and mortality. In central British Columbia, stand structure, species composition, and growth rates in complex spruce–fir forests can be maintained through partial-harvest silviculture treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".