Regional characteristics drive thinning effects on boreal soil organic carbon stocks
Bibliographic record
Abstract
Boreal forest soils store more carbon than the aboveground vegetation and play a critical role in the global carbon cycle and ecosystem function. Despite their importance, the long-term impacts of forest management on soil organic carbon (SOC) remain unclear, especially in boreal forests with contrasting climatic conditions. This study assesses the effects of commercial thinning on SOC stocks (20-year post-treatment) in black spruce stands across two boreal regions in Québec: the warmer, drier Abitibi and the colder, wetter Cote-Nord, accounting also spatial variability from skidding strips. We measured SOC stocks at three soil depths (forest floor, mineral layer: 0–15 and 15–30 cm) in both thinned and unmanaged stands, using bulk density and SOC concentration. Soil pH, cation exchange capacity (CEC), and macronutrients were also conducted to characterize soil fertility. Thinning had no effect on total SOC stocks of the whole profile in either region, but horizon-specific differences were detected. A decrease in forest floor SOC stocks was observed in Côte-Nord, whereas SOC stocks in Abitibi remained similar. These contrasting outcomes reflect regional differences in soil fertility (higher CEC, potassium in Abitibi) and climatic conditions which may have influenced vegetation regrowth and, consequently, SOC stocks’ recovery over time. Although SOC stocks decreased in skidding strips, their small proportion within the stand minimized their effect on total SOC stocks at the plot level. Overall, the study emphasizes the need to consider climatic and soil context, as well as limited spatial extent of skidding strips, in evaluating silvicultural impacts on boreal carbon sequestration.
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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".