Restoring forest cover at diverse sites across Canada can balance synergies and trade-offs
Bibliographic record
Abstract
Swift action to restore forests is critical for mitigating climate change and preserving biodiversity. Canada has an ambitious program to plant two billion trees to help exceed the country's emissions targets while restoring forest habitat and providing social and economic benefits. We conducted a systematic analysis of where new tree cover can maximally achieve these benefits while minimizing implementation costs. Accounting for critiques of global restoration mapping that include the overestimation of mitigation potential and inadequate biodiversity and social safeguards, we find that 19.1 Mha are available, which is much more than the approximately 1.2 Mha needed to plant two billion trees. Optimization scenarios for 1.2 Mha revealed synergies and trade-offs. Scenarios prioritizing low costs, accessibility, and high growth are concentrated in temperate and coastal areas, overlapping partly with biodiversity scenarios, but with trade-offs of higher costs. A diverse portfolio of regionally restored sites, each tailored for specific attributes, is most likely to deliver multiple benefits at the pace demanded by the current crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".