Moving beyond forest cover: Linking forest density, age, and fragmentation to diet
Bibliographic record
Abstract
Forests support food security and nutrition worldwide, especially so for highly forest-dependent communities who collect a variety of food products from nearby forests. While the importance of forest cover to the diets of forest-dependent communities has been well-researched, little is known regarding the role of more specific forest characteristics - information that would be valuable for better identifying the landscapes that support a nutritious and diverse diet. To address this research gap, we linked child dietary data to remotely-sensed geospatial indicators of surrounding forest characteristics - using more nuance than is typically undertaken - by examining forest age, tree density, and forest fragmentation in Kenya's East African Montane Forests. Interestingly, dietary diversity of children demonstrated no or relatively weak associations with forest characteristics. However, by parsing out individual food groups, we exposed the nuance and complexities associated with the forest-diet relationship. Vegetable/fruit consumption was positively associated with open and moderately dense forest cover, but negatively associated with fragmented forest cover. The consumption of meat and vitamin A-rich fruit was positively associated with younger forest cover, and negatively associated with dense forest cover. Older forest cover was positively associated with green leafy vegetable consumption, but negatively associated with other vegetable/fruit consumption. Our findings provide suggestive evidence that there is no single 'ideal' type of forest for supporting food security and nutrition - rather, different types of forests are associated with different dietary benefits. Taken together, these results indicate the need for more in-depth research that accounts for factors beyond the proximity and amount of generic forest cover.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".