Do harvest methods and soil type impact the regeneration and growth of black spruce stands in northwestern Quebec?
Bibliographic record
Abstract
Machinery traffic restrictions during forest harvest have been adopted to minimize soil damage and protect tree regeneration. However, this practice is questioned for paludifying black spruce ( Picea mariana (Mill.) BSP) stands in which severe soil disturbance by wildfire restores forest productivity. The objective of this study was to determine, 8 years after harvest, how soil disturbance created by clearcutting and careful logging affected black spruce natural regeneration and growth and how this effect varied by soil type. While regeneration density was higher following careful logging, stocking was not influenced by harvest method. Regenerating stands were taller following clearcutting despite potentially greater damages to preestablished regeneration. Compared with careful logging, clearcutting also resulted in reduced cover of Sphagnum spp. and ericaceous shrubs. Spruce stem density and stocking were both higher on organic and subhydric soils and lower on mesic soils. No significant interactions were observed between harvest method and soil type, indicating that the observation of taller black spruce stands and adequate stocking with clearcutting may be applicable to all soil types considered in this study. These results suggest that an adequate level of soil disturbance is an important part of forest regeneration, particularly in ecosystems where an autogenic reduction in productivity occurs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".