Yellow Mealworm Larvae (Tenebrio molitor) Fed Mycotoxin-Contaminated Wheat—A Possible Safe, Sustainable Protein Source for Animal Feed?
Bibliographic record
Abstract
The aim of this study was to determine the potential for accumulation of deoxynivalenol (DON) in yellow mealworm larvae (Tenebrio molitor) reared on high DON Fusarium-infected wheat and investigate the effects on production, survival and nutritional traits. Wheat containing 200 μg/kg DON was used as the control diet. A different source of wheat was sorted into six fractions and mixed to obtain low (2000 μg/kg), medium (10,000 μg/kg) and high (12,000 μg/kg) levels of DON. Each diet was replicated five times with 300 or 200 mealworms per replicate for the feeding and breeding trials, respectively. Trial termination occurred when the first two pupae were observed (32–34 days). There was no difference in the concentrations of DON detected in the larvae between diets that ranged from 122 ± 19.3 to 136 ± 40.5 μg/kg (p = 0.88). Excretion of DON was 131, 324, 230 and 742 μg/kg for control, low, medium and high, respectively. Nutritional analysis of larvae showed maximum crude protein of 52% and crude fat of 36%. Ash, fiber, chitin, fatty-acids and amino-acid content were consistent across diets. Survival was greater than 96% for all life stages and average daily gain ranged from 1.9 ± 0.1 to 2.1 ± 0.1 mg/day per mealworm. Larvae accumulated low levels of DON from Fusarium-infected wheat diets suggesting contaminated wheat could be used to produce a sustainable, safe protein source.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".