Forests and cycles of agrarian sustenance: time-to-event analysis of ecosystem provisioning services and seasonal food insecurity
Bibliographic record
Abstract
In Savanna ecosystems, cycles of sustenance include periods of food abundance and deficits. In rain-fed agricultural systems, forests are a vital food source that helps households close this cycle. This paper uses cross-sectional data to explore the association between provisioning ecosystem services and timing to food insecurity in forest fringe communities (N = 500). The time to food insecurity analysis revealed a significant difference in the onset of food insecurity among households with varying levels of access to forest provisioning services. Households with access to more than four forest products experienced a slower timing to seasonal insecurity. In contrast, those with access to less than two experienced a faster seasonal food insecurity onset. The time-to-event analysis further showed that as the number of provisioning services households access increased, households experienced delayed seasonal food insecurity. Access to multiple services allows households to combine them to provide nutritious food during lean seasons when food supplies and income are depleted. Our findings suggest that economic and agronomic factors, including wealth, farm size and number of farms, mediate the onset of food insecurity among smallholders. Our findings reinforce the need for a rights-based approach to forest management that prioritizes local stewardship against forest enclosures.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".