Black spruce growth response to varying levels of biomass harvest intensity across a range of soil types: 15-year results
Bibliographic record
Abstract
With a growing interest in the diversification (e.g., bioenergy, biochemicals) of the forest industry beyond the traditional product streams, concerns that higher harvest utilization levels may compromise site productivity have been heightened. This study reports on 15-year tree growth responses to varying levels of biomass removals conducted on four soil types: loamy tills, outwash sands, wet mineral, and peatlands. Experimental harvest treatments included stem-only, full-tree, full-tree chipping (a full-tree harvest with the roadside material chipped and returned to the site), and full-tree + bladed (a full-tree harvest followed by forest floor removal). Results indicated no significant effect on height growth on the loamy tills, a significant decline for the blading treatment on the sandy soils, and an increase when the blading treatment was applied to the peatland sites. At the stand level, better planted seedling survival and higher recruitment of naturals on the more extreme removal treatment (forest floor removal on sandy sites) tended to nullify any negative impacts identified in the individual-tree growth measurements. The more than doubling of the slash loading on the stem-only treatment plots compared with the full-tree plots did not result in differences in tree productivity levels between these two operational treatments. The stands, however, were just approaching crown closure by year 15, suggesting that ongoing monitoring will be required to confirm that the growth trajectories for the various harvest treatment – soil type combinations can be maintained.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".