Upland black spruce stand development 17 years after cleaning and precommercial thinning
Bibliographic record
Abstract
Density management is often used by silviculturists to guide stand composition and development. We examined the effects of cleaning (hardwood removal) and four levels of precommercial thinning (0, 20, 35 and 50% basal area removal) on stand development in a dense, 24-year-old upland black spruce (Picea mariana [Mill.] BSP) plantation near Beardmore, Ontario. Immediately before treatment, stand densities and basal areas for all species and for black spruce averaged 7375 and 6415 stems ha-1, and 27.9 and 20.5 m2 ha-1, respectively. Seventeen years after treatment, black spruce total stand volume (VT) was higher in the cleaned, unthinned plots (243 m3 ha-1) than in the untreated controls (171 m3 ha-1) while total stand volume increment of all species combined was similar in these two treatments. Compared with cleaning alone, thinning cleaned plots from below increased quadratic mean diameters (DQ) by up to 9% but decreased VT by up to 28%. At plantation age 41, increases in black spruce densities of 1000 stems ha-1 resulted in mean decreases of 0.6 cm in DQ and mean increases of 43 m3 ha-1 in VT. Endemic black spruce stem mortality rates decreased with thinning intensity, with mortality concentrated in the smallest size classes. In some plots, mortality was increased by wind or snow damage, and by root rots. Height increment of dominant trees was unaffected by thinning. Projected yields at age 55 (the physical rotation age—the age at which maximum mean annual increment occurs) suggest the heaviest precommercial thinning could increase quadratic mean diameter from 16.1 to 17.7 cm, but decrease merchantable stand volume from 292 to 225 m3 ha-1. Results indicate that total black spruce fibre yields and product value on these sites will be maximized in denser stands. Cleaning appears to offer greater benefits for black spruce fibre production than precommercial thinning. Key words: black spruce, precommercial thinning, cleaning, density management, projected yields
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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) | 0.001 | 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".