Carbon capture capacity estimation of taiga reforestation and afforestation at the western boreal edge using spatially explicit carbon budget modeling
Bibliographic record
Abstract
Canada's northern boreal has considerable potential for tree planting related climate change mitigation solutions, considering the sparsity of trees and large portions of non-forested land at the northern forest edge. Moreover, afforestation at the northern boreal edge would enable further the observed gradual tree-line advancement of the taiga into the southern arctic, assisting forests in their migration northward while capitalizing on their carbon capture capacity. However, significant uncertainties remain about the carbon capture capacity of large-scale tree planting in the northern boreal ecozones under changing climatic conditions due to lack of spatially explicit ecozone specific modeling. In this paper, we provide monte carlo estimates of carbon capture capacity of taiga reforestation and afforestation at the north-western boreal edge using spatially explicit carbon budget modeling. We combine satellite-based forest inventory data and probabilistic fire regime representations to simulate how total ecosystem carbon (TEC) might evolve from 2025 until 2100 under different scenarios composed of fire return intervals (FRI), historical land classes, planting mortality, and climatic variables. Our findings suggest that afforestation at the north-western boreal edge could provide meaningful carbon sequestration toward Canada's climate targets, potentially storing approximately 3.88G Tonnes of $CO_{2}$e over the next 75 years in the average case resulting from afforestation on approximately 6.4M hectares, with the Northwest Territories (NT)-Taiga Shield West (TSW) zone showing the most potential. Further research is needed to refine these estimates using improved modeling, study economic viability of such a project, and investigate the impact on other regional processes such as permafrost thaw, energy fluxes, and albedo feedbacks.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".