Carbon sequestration and storage implications of three forest management regimes in the Wabanaki-Acadian Forest: A review of the evidence
Bibliographic record
Abstract
Forests contain substantial carbon stores, including above and below ground, living and non-living biomass. Different management regimes produce different outcomes related to stored and sequestered carbon in forests. The geographic focus of this paper is the Wabanaki-Acadian Forest of the Maritime Provinces of Canada (New Brunswick, Nova Scotia, and Prince Edward Island). This manuscript reviews literature to evaluate the carbon impacts of (1) intensive forest management for fiber products, (2) unharvested (or conservation) forest, and (3) climate-focused, ecological forestry. Each of these forest management strategies and concomitant silviculture regimes sequester and store carbon at varying rates and across different carbon pools in the forest. The literature suggests that unharvested (conservation) forests store and sequester the most carbon, and traditional, intensive fiber management stores and sequesters the least. Ecological forestry may provide the best balance between carbon sequestration and storage and climate adaptability, while also allowing for the provision of some timber/fibre products. This paper also discusses the co-benefits offered by forests under each of the three management regimes. New research, in general and in the region, needs to examine further below-ground carbon dynamics in soil as most efforts to document carbon focuses on above ground carbon pools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".