Edible insects: cricket farming and processing as an emerging market
Bibliographic record
Abstract
This article provides information on recent trends in cricket farming and processing in Asian and Western countries. Whilst eating insects collected from the wild has long been a common practice in many countries, farming and transforming insects into a food ingredient for packaged products is a new development. Particularly in North America and Europe, some new, small companies are transforming cricket (and mealworm) powder into packaged food (energy bars, pasta, and chips among the examples). Within this article, two contrasting farming systems are principally considered. On one hand is the Thai cricket farming model, based on micro-farms, in which the small farmers do not make the flour; this task instead being handled by specialised businesses. On the other hand, is the western farming model, in which farms are large, and the flour is produced by the very same factory-farm. Examples of this model are found in the Netherlands (Protifarm) and Canada (Entomofarm). Since insect powders (flour) in packaged foods represent a new category of food product, little market data and/or surveys are available. The products are often sold on small online shops, within the context of an informal business operations. As a consequence, some of the information in this article comes from informal sources or the direct experience of the author.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".