Forest regrowth improves people’s dietary quality in Nigeria
Bibliographic record
Abstract
Abstract Two billion people currently suffer from micronutrient deficiencies. Existing literature shows that forests can improve people’s dietary quality—yet forests are often overlooked in food security policies, which focus primarily on the production of staple crops. The Bonn Challenge has set a goal of restoring 350 million ha of forest by 2030, but it remains unclear whether restored forests will exhibit the species diversity needed to improve diets in the same way as existing forests. Here, we report how forest regrowth in Nigeria has affected people’s dietary quality. We combine a new map on forest regrowth with food consumption panel data from over 1100 households—and use a combination of regression and weighting analyses to generate quasi-experimental quantitative estimates of the impacts of forest regrowth on people’s food intake. We find that people living in areas where forest regrowth has occurred have a higher intake of fruits and vegetables and thus higher dietary diversity.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".