Do biomass removal and structure-enhancing treatments influence deadwood characteristics following commercial thinning in spruce plantations in New Brunswick, Canada?
Bibliographic record
Abstract
Effects of commercial thinning on downed coarse woody debris (CWD) and standing dead trees (snags) were examined in six intensively managed 22- to 30-year-old white spruce (Picea glauca (Moench) Voss) plantations in northern New Brunswick, Canada. Four alternative treatments were applied: (i) an unthinned control and three commercial thinning 40% basal area removals, with (ii) slash and tops remaining on the site (status quo), (iii) most of the branches and tops extracted from the site (biomass removal), and (iv) clumps of unthinned trees left, with one-half of the trees in each clump girdled to create snags (enhanced structure). Three years after thinning, CWD volume increased by 14%–27% in the status quo and enhanced structure treatments, by 6% in the biomass removal treatment, and by 0.1% in the unthinned treatment. Mean snag volume changed little, i.e., less than 1 m3·ha−1 among treatments, and stumps of thinned trees were 4.6–4.9 m3·ha−1. The girdled trees added 0.7 m3·ha−1 of snags, and 1.3 m3·ha−1 is expected to be added from girdling the remaining live trees in the unthinned clumps in 2016. The girdled trees and future to-be-girdled trees added up to 50% of the new deadwood in the enhanced structure treatments, but thinning treatments did not significantly affect total deadwood. The results indicate that commercial thinning produced fine debris but had little effect on overall deadwood amount. Girdling trees during commercial thinning, at the intensity used in this study, is unlikely to produce sufficient snags. Leaving islands and clumps during harvest before plantation establishment would be a more effective way of adding structure.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".