Late-entry commercial thinning effects on Pinus banksiana: growth, yield, and stand dynamics in Québec, Canada
Bibliographic record
Abstract
We studied late-entry commercial thinning effects on growth, yield, and regeneration in a 48-year-old jack pine ( Pinus banksiana Lamb.) stand. Applied thinning intensities were 27, 32, and 47% of merchantable basal area (BA) excluding skidding trails. After 15 years, mean diameter at breast height of surviving trees in the 47% BA removal increased by 4.9 cm (25%) compared to the unthinned control. The 47% BA removal also increased gross merchantable volume (GMV) tree −1 by 46% compared to the control. The 27% BA removal had twice as much GMV ha −1 compared to the 47% BA removal after 15 years. Moreover, cumulative GMV ha −1 was much higher in the 27% BA removal than in the unthinned control. The highest thinning intensity produced larger trees on average, while the lowest thinning intensity maximized volume production per hectare. Maintenance of acceptable growing stock throughout the 15-year period in the 27% BA removal could provide other ecosystem functions such as biodiversity enhancement or wildlife habitat by delaying senescence. Regeneration data showed that a shift in species composition occurred in the understory. After 15 years, the understory was dominated by black spruce ( Picea mariana (Mill.) B.S.P.), white birch ( Betula papyrifera Marsh.), and trembling aspen ( Populus tremuloides Michx.). If regenerating jack pine is an objective after final overstory removal, additional efforts will be needed to re-establish this species.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".