The impact of small-scale cricket farming on household nutrition in Laos
Bibliographic record
Abstract
We examined the potential of cricket farming as an innovative solution to improving household nutrition in Laos, where edible insects are already part of traditional diets. We conducted research with a total of 40 rural households in Central Laos, in which small-scale cricket farming was introduced to 20 intervention households. Nutritional situation and changes of all households and cricket production and consumption of the intervention households were assessed. Malnutrition was prevalent amongst the study population and we found indications for dietary inadequacies. Despite fluctuating harvest results and some production failures, most intervention households successfully produced and harvested crickets over five production cycles. Cricket farming was not only appreciated by the project participants, but also spread to non-project households. 70% of the total cricket harvest were used for own consumption and crickets were eaten by all family members, including small children and women, in amounts that improved nutritional adequacy during the brief period following harvest. To increase the nutritional impact, production has to be stabilised and adapted to provide a more continuous supply of crickets over the year.
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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 | 0.001 |
| 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".