The Role of Agroforestry in Supporting Food Security in Small Islands (Case in Pahawang Island, Indonesia)
Bibliographic record
Abstract
Agroforestry has many benefits, especially in improving the economy and food security. The purpose of this study was to analyze food availability and the level of community food security in Pahawang Island, Indonesia. The data were analyzed descriptively and based on the House hold food security access scale (HFSAS) on 9 variables. The results showed that the food provided from the agroforestry area consisted of vegetables, fruits, tubers and empon-empon while the food that could not be provided from the agroforestry area was rice, fish, tempeh, tofu, chicken, meat and others. etc. are obtained from the sale of agroforestry products. The revenue from this agroforestry product reaches Rp. 641,085, 000 per year or equivalent to 64,108.5 kilograms of rice per year. This means that for 14 days the community can survive if Pahawang Island experiences a disaster that causes people to be unable to leave the island. Based on the calculation results, the average food security score of Pahawang Island is included in the category of moderately food security with a score of 15.6. Agroforestry management in Pahawang Island needs to be maintained because it has proven to be able to improve community food security. The addition of commercial plant species also needs to be done, especially from the types of fruits and woody plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".