The potential for natural forest regeneration in tropical regions
Bibliographic record
Abstract
Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits. Yet achieving forest restoration at scale requires cost-effective methods. Tree planting in degraded landscapes is a popular but costly forest restoration method, which often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions. Here, we assess the current spatial distribution of pantropical natural forest (from 2000-2016) and use this information to present the first model of the potential for natural regeneration across tropical forested countries and biomes at 30-meter spatial resolution. We estimate that 215 million hectares - an area greater than the entire country of Mexico - have potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt CO2 (range 21.1-25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico, and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalise on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas. File descriptions: pnv_pct_30m - files named with this prefix are the 30m continuous probability predictions of the potential for natural regeneration, stored as integers representing percentages to minimise file sizes tile_index_number_map.jpg – corresponding locations of the ‘pnv_pct_30m’ tiles prop pnv v1 1km.tif - for display purposes only (not used in the Williams et al. analysis) the approximately 1km resolution overview raster representing the proportion of the area of each pixel that has the potential for natural regeneration pnv_bin_30m.zip - this .zip folder contains the 30m resolution binary predictions (where a value of >0.5 is allocated a value of 1, acknowledging users may prefer to use a different threshold for their context) of areas suitable for natural regeneration The 30m resolution datasets have been tiled into 10 degree latitude/longitude tiles
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".