Billions and billions sold: Pet-feeder crickets (Orthoptera: Gryllidae), commercial cricket farms, an epizootic densovirus, and government regulations make for a potential disaster
Bibliographic record
Abstract
The cricket pet food industry in the United States, where as many as 50 million crickets are shipped a week, is a multi-million dollar business that has been devastated by epizootic Acheta domesticus densovirus (AdDNV) outbreaks. Efforts to find an alternative, virus-resistant field cricket species have led to the widespread USA (and European) distribution of a previously unnamed Gryllus species despite existing USA federal regulations to prevent such movement. We analyze and describe this previously unnamed Gryllus and propose additional measures to minimize its potential risk to native fauna and agriculture. Additionally, and more worrisome, is our incidental finding that the naturally widespread African, European, and Asian “black cricket,” G. bimaculatus, is also being sold illegally in southern California pet food stores. We assayed crickets of all five USA and European commercial species for presence of the AdDNV to document extent of the infection—all five species can be infected with the virus but only A. domesticus is killed. Based on its already cosmopolitan distribution, apparent inability to live away from human habitation, and resistance to AdDNV, we suggest that Gryllodes sigillatus is the best-suited replacement cricket for commercial production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".