Protect young secondary forests for optimum carbon removal
Bibliographic record
Abstract
Abstract Avoiding severe global warming requires large-scale removals of atmospheric carbon dioxide. Forest regeneration offers cost-effective carbon removals, but annual rates vary substantially by location and forest age. Here we generate grid-level (~1-km 2 ) growth curves for aboveground live carbon in naturally regrowing forests by combining 109,708 field estimates with 66 environmental covariates. Across the globe and the first 100 years of growth, maximum carbon removal rates varied 200-fold, with the greatest rates estimated in ~20- to 40-year-old forests. Despite a focus on new forests for natural climate solutions, protecting existing young secondary forests can provide up to 8-fold more carbon removal per hectare than new regrowth. These maps could help to target the optimal ages and locations where a key carbon removal strategy could be applied, and improve estimates of how secondary forests contribute to global carbon cycling.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".