Long-Term Soil Fertility and Site Productivity in Stem-Only and Whole-Tree Harvested Stands in Boreal Forest of Quebec (Canada)
Bibliographic record
Abstract
Using residual biomass from forest harvesting to produce energy is viewed increasingly as a means to reduce fossil fuel consumption. However, the impact such practices on soil and future site productivity remains a major concern. We revisited 196 forest plots that were subject to either whole-tree (WTH) or stem-only (SOH) harvesting 30 years ago in the boreal forest in Quebec, Canada. Plots were stratified by four soil regions grouped by so-called ‘soil provinces’. Soil analyses indicated that after 30 years, the forest floor of WTH sites had smaller pools of N (−8%), exchangeable Ca (−6%) and exchangeable Mn (−21%) and a higher C/N ratio (+12%) than that of SOH sites. Mineral soil responses to the two harvesting intensities differed among soil provinces. In the two coarse-textured granitic soil provinces, organic matter, organic carbon, and nitrogen pools over the whole solum (0–60 cm soil depth) were at least 28% smaller after WTH than after SOH. Site productivity indicators followed differences between soils and were lower after WTH than after SOH in the two granitic soil provinces. The study shows that soil characteristics greatly influence a soil’s sensitivity to increased forest biomass harvesting in the long term.
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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 | 0.001 | 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) | 0.001 | 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".