Novel carbon dynamics assessment framework reveals climate positive land management approaches across North America
Bibliographic record
Abstract
Natural climate solutions (NCS) can foster ecosystem resiliency and mitigate climate change. However, a gap exists between carbon cycle science and decision support systems. Bridging this gap can advance climate positive land management and promote the implementation of natural climate solutions. Here, we propose a novel framework to assess carbon dynamics to reveal management approaches for land cover at a 300 m resolution within which natural climate solutions can be more readily identified and applied. We use annual land cover change and model the associated carbon dynamics to categorize the sub-regional ecodistricts of Canada and the USA into the most effective NCS category based on its current change dynamics. Restoration NCS may be a useful strategy in the southern boreal forest, southeastern USA (Eastern Temperate Forest), and western Canadian Cordillera, while Protection NCS should be focused in the Great Plains and the far North. Specifically, we find that 9 % of ecodistricts, and 23 % of land area, would be best managed through protection-based nature climate solutions, while 27 % of ecodistricts, and 26 % of land area, should be managed using restoration climate solutions. This transferable framework can be used to target the sub-regional implementation of different NCS strategies based on local-level land cover changes and carbon dynamics. • Novel framework integrates LCC & carbon modeling to propose NCS management solutions. • Framework can track effectiveness of implemented natural climate solutions over time. • 23 % of North America suited for protection-based climate solutions. • 26 % of area requires restoration solutions, with 5 % needing immediate prioritization.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".