Above- and belowground carbon stocks under differing silvicultural scenarios
Bibliographic record
Abstract
Despite the need for climate change mitigation and altered forest management practices, little is known about the impacts of silvicultural practices such as partial-cuts and clear-cuts on forest ecosystem carbon (C) dynamics. Specifically, the effect of these two overstory treatments on C pools other than the aboveground biomass of trees remains poorly understood. Here, C stocks were estimated for a northern temperate mixed forest located in eastern Québec, Canada, five years after clear-cutting and partial-cutting, either with or without a brushing treatment to control the competing vegetation. The biomass of the aboveground vegetation (trees, saplings, understory), litter and woody debris (coarse, small, fine), as well as the roots (diameter ≤ 1.5 cm) was evaluated. Additionally, soil C pools up to a depth of 35 cm of the mineral soil were assessed. Total ecosystem C stocks were influenced by the overstory treatments reflecting harvest intensities. Although the belowground C pools were major contributors to total ecosystem C stocks, silvicultural treatments only influenced forest floor and aboveground C stocks. However, assessments like the one presented here capture contemporary C stocks, which highlights the need for monitoring to build suitable forest ecosystem C models and to understand long-term C dynamics.
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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 | 0.001 | 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".