Larger diet particle sizes cause crickets to grow faster with no effect on final body size
Bibliographic record
Abstract
Artificial diets are costly to produce, so diet efficiency is critically important to the success of mass rearing insects. One way to improve feed efficiency is through dietary particle size optimization. We used a commercially reared species, Gryllodes sigillatus , to test whether individual crickets reared from hatch to adulthood on diets of different particle sizes would grow differently. Crickets fed a diet ≥0.5 mm grew heavier during the first three weeks but weighed the same after six weeks regardless of diet size. We then provided crickets with a choice of particle size throughout development to test for dietary size preference. Given a choice, crickets consumed the most food from the 1.0-1.4 mm diet. Crickets also preferentially select ingredients from mixed diets, so to test whether grinding a conventional diet to a finer particle size could influence performance traits, we ran a large-scale group rearing experiment and found no effect of further grinding on colony mass gain or development time. Pelleting diet is another method for eliminating self-selection of ingredients, and so we tested whether pelleting finely ground conventional cricket feed would result in any substantial changes to the developmental life history of individual crickets. Crickets fed a 2 mm pelleted diet grew larger body size but were not significantly heavier. Overall, our results demonstrate that particle size optimization can be leveraged to enhance cricket life history traits important to mass production, as growth was accelerated on larger particle size diets and crickets preferred to eat larger-sized diets. Researchers focusing on physical properties of insect diets should carefully consider the timing of growth and development through which diet particle size may influence feed efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".