Aboveground carbon stock of scarified black spruce stands—a 20-year study in boreal ecosystems
Bibliographic record
Abstract
Scarification followed by planting is a widely used silvicultural practice in eastern North American boreal forests to promote black spruce ( Picea mariana) regeneration after clear-cutting, especially in ericaceous-dominated stands where tree growth is limited. Given the role of these forests as carbon sinks, we evaluated the medium-term impact of scarification on aboveground carbon stocks. We conducted vegetation inventories and carbon stock calculations in ∼20-year-old experimental plantations in Québec, Canada. Scarified plots were compared to naturally well-regenerated clearcuts and to non-scarified plantations in two contrasting regions: the colder, wetter Côte-Nord, with dense ericaceous cover, and the warmer, drier Abitibi, with lower ericaceous abundance. Scarification increased tree carbon stocks in sites where advance regeneration was limited. However, it also caused a long-lasting reduction in understory biomass, particularly bryophytes, with limited recovery after two decades. This shift in carbon allocation—from understory to trees—resulted in similar or higher total aboveground carbon stocks in scarified plots, especially in the wetter region where ericaceous competition was strongest. These findings emphasize the importance of considering both climate and initial site conditions in silvicultural planning. They also highlight the role of understory vegetation in carbon cycling and the trade-offs between forest productivity and biodiversity conservation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".