Bibliographic record
Abstract
- Currently, 1 trillion to 1.2 trillion insects are raised on farms annually for food and animal feed.- There are currently between 79 billion and 94 billion insects alive on farms globally on average on an average day.- While it is unclear what welfare reforms might best improve the lives of insects on farms, it seems possible that standardized training on best practices, and potentially slaughter reform are promising ways to improve insect welfare on farms.- The countries that farm the most insects in the world are Thailand, France, South Africa, China, Canada, and the United States.- The industry is rapidly growing —millions of dollars have been invested into startups that are working to industrialize the industry, especially to produce insect alternatives to animal feed and fishmeal. This also means that the scale could increase by one or more orders of magnitude in the near future.- Note that these estimates only include insects whose bodies are eaten in whole or powdered form for food and animal feed. They do not include insects farmed for a food product they produce (such as honey bees), nor insects who have a food additive produced with a minor derivative of their bodies (such as cochineals). This research also does not cover wild insects collected for food or animal feed. Finally, this research does not cover annelids raised for fishing bait, though some of the insects sold live described in this report are likely used for fishing bait.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".