Precommercial Thinning Increases Spruce Yields in Boreal Mixedwoods in Alberta, Canada
Bibliographic record
Abstract
A few studies in young mixedwood stands demonstrate that precommercial thinning of aspen at early ages can improve the growth of spruce and increase stand resilience to drought. However, information on tree and stand responses to thinning in older mixedwood stands is lacking. To address this need, a study was initiated in 2008 in Alberta, Canada in 14 boreal mixedwood stands (seven each at ages 17 and 22). This study investigated growth responses following thinning of aspen to five densities (0, 1000, 2500, 5000 stems ha−1 and unthinned (control)). Measurements were collected in the year of establishment, and three and eight years later. Mortality of aspen in the unthinned plots was greater than in the thinned plots which were not significantly different amongst each other. Eight years following treatment, aspen diameter was positively influenced by thinning, while there was no effect on aspen height. The density of aspen had no significant effect on the survival of planted spruce. Spruce height and diameter growth increased with both aspen thinning intensity and time since treatment. Differentiation among treatments in spruce diameter growth was evident three years from treatment, while differentiation in height was not significant until eight years following treatment. Yield projections using two growth models (Mixedwood Growth Model (MGM) and Growth and Yield Projection System (GYPSY)) were initialized using data from the year eight re-measurements. Results indicate that heavy precommercial aspen thinning (to ~1000 aspen crop trees ha−1) can result in an increase in conifer merchantable volume without reducing aspen volume at the time of harvest. However, light to moderate thinning (to ~2500 aspen stems ha−1 or higher), is unlikely to result in gains in either deciduous or conifer merchantable harvest volume over those of unthinned stands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".