Aspen and Spruce Densities Affect Tree Size, Future Stand Volume, and Aboveground Carbon Following Precommercial Thinning
Bibliographic record
Abstract
Data collected over a 30-year period from an experiment replicated across 21 locations in western Canada are used to explore the effects of precommercial thinning of trembling aspen to a range of densities in combination with three initial white spruce densities on tree growth and stand dynamics. Increasing differentiation amongst the 15 treatments was observed with age after thinning for both spruce and aspen responses. Spruce height and diameter declined with increasing aspen density. At age 10 spruce diameter with no aspen was 1.5× that of spruce in unthinned while it was 2.6× that of spruce in unthinned at age 28. Following thinning aspen DBH and crown width declined with increasing density of retained aspen while slenderness and height to crown base of aspen increased. Thinning to 1500 aspen·ha−1 resulted in aspen DBH that was 22% larger relative to the unthinned at age 28. Spruce volume and stemwood biomass at age 90, estimated using the Mixedwood Growth Model (MGM21), declined with increasing initial aspen density, with the complete removal of aspen resulting in nearly double the spruce volume of unthinned plots. However, total stand volume, total stand biomass, and stemwood carbon at age 90 are predicted to be largest in mixed stands with aspen densities of 1500 stems·ha−1 or higher.
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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) | 0.000 | 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".