Prioritizing ecological restoration of converted lands in Canada by spatially integrating organic carbon storage and biodiversity benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Ecosystem restoration is a fundamental way of delivering nature‐based solutions to improve resilience in a changing climate and sustain biodiversity. Spatial analyses to identify where ecosystem restoration would yield targeted environmental benefits are critical to inform, and coordinate restoration initiatives at multiple scales to achieve national commitments and global goals. Here, we provide an optimization analysis for restoration potential of converted terrestrial ecosystems in Canada by integrating carbon storage and biodiversity benefits as key considerations. Our results show that converted landscapes are prevalent in southern anthropic regions of Canada, with the greatest potential for biodiversity benefits through forest and grassland restoration. At national scales, carbon density (tonnes C/km 2 ) and total carbon storage (tonnes C) potential were greatest for wetland and forest restoration, respectively. When biodiversity and carbon were both included in an optimization framework, consistent priorities across all three restoration targets (50,000; 100,000; and 150,000 km 2 ) comprised forest restoration in the St. Lawrence and Lake Erie Lowlands, with the Lake Manitoba Plains, Interlake Plains, and Manitoulin‐Lake Simcoe ecoregions also frequently identified. Our analysis will help decision‐makers identify where restoration of converted lands may support considerable gains in simultaneously achieving climate and biodiversity goals in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it