Climate benefits of afforestation and reforestation with varying species mixtures and densities in the north-western boreal lands
Bibliographic record
Abstract
Abstract The boreal forest is a vital carbon sink. Using 250 year simulations for Canada’s Taiga Plains, a priority of the 2 billion trees program, we tested afforestation and reforestation (A/R) strategies that combine species mix, planting density and surface albedo. Medium-density (600–1400 trees ha −1 ) mixed stands with ∼25%–40% deciduous trees stored 15%–30% more net ecosystem carbon than conifer monocultures by coupling rapid early growth with long-term retention and greater disturbance resilience. Replanting under-stocked stands with these mixtures raised long-term storage by 18%–30% over business-as-usual. Accounting for albedo showed pure evergreen or deciduous stands lost 6%–20% of their climate benefit, whereas mixed stands yielded net cooling and the highest sequestration (≈ 4.6–4.7 tCO ₂ e ha −1 yr −1 ). Partial harvesting plus replanting preserved, and sometimes increased, ecosystem carbon (≈ 300–340 tC ha −1 ) and productivity (≈ 1.6–2.0 tC ha −1 yr −1 ) without raising risk. Blending fast-growing deciduous trees with long-lived conifers at intermediate density maximizes boreal A/R climate value and informs reforestation policy elsewhere.
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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) | 0.000 | 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 | 0.001 |
| 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".