Changes in wood density, growth, and carbon storage of the main stem of planted white spruce (Picea glauca) after commercial thinning
Bibliographic record
Abstract
Commercial thinning, often included in silvicultural scenarios applied to plantations, influences tree growth and wood properties and, consequently, can modify the carbon sequestration rate of tree stems. The present study considered the wood density variations within and between tree stems to estimate carbon dioxide (CO2) sequestration in the stem under four treatments—control, thinning from below, early release of 50 crop trees per hectare, and 100 crop trees per hectare—conducted in white spruce (Picea glauca) plantations in eastern Quebec. The plantations dating from 1990 were thinned in 2008. First, disk samples collected in 2021 from 140 trees across the four thinning intensities were used to construct a ring-density model following thinning. The plot inventory data from 2008, 2014, and 2021, combined with the ring-density model, were then used to estimate individual tree carbon sequestration, which was summed at the plot level. We found that (1) ring density displayed higher values near the pith, followed by a rapid decline, after which ring density slightly increased toward the bark; (2) thinned and control treatments showed similar average ring-density chronologies throughout the study period, constraining the generalization of post-thinning ring-density trends; and (3) thinned plots exhibited lower tree CO2 sequestration rates than control plots, with estimates of 5.17, 5.35, 4.75, and 5.84 t·ha−1·year−1 for 100 crop trees per hectare, 50 crop trees per hectare, thinning from below, and the control respectively. This study provides insights into how thinning impacts carbon dynamics in the tree stems of young stands, which can be used to weigh the trade-offs between active management and carbon storage.
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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 | 0.000 |
| 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) | 0.000 | 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".