Bibliographic record
Abstract
Abstract Forests are the largest source of terrestrial biodiversity and contain over half of the world’s terrestrial plant and animal species. In addition to the multitude of species supported by forest systems, forests are also responsible for life-sustaining ecological processes such as nutrient cycling and water regulation. As a rich source of plants, animals, soil, and water, the connection between forests and food seems undeniable. However, until recently, this relationship has been poorly understood. In this review, we explore three main pathways in which forests contribute to food security and nutrition. These include: (1) a direct consumption pathway, (2) an income pathway and (3) an agroecological pathway. We find the following: (1) forests contribute directly to people’s diets through the harvest of bushmeat, wild fruits and other forest-sourced foods; (2) the sale of non-timber forest products contribute to people’s income, enabling the purchase of a diversity of food items from markets; and finally (3) forests and trees support diverse crop and livestock production through an array of ecosystem services such as pollination, soil fertility and water and climate regulation. Our findings shed light on the vital role that forests play in food security and we conclude that further research is needed to understand the interactions between the ecological, socioeconomic and cultural dimensions of forests and diets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".