Impact of ergot-contaminated feed on yellow mealworm larvae performance and diet preference
Bibliographic record
Abstract
This study aimed to determine if ergot alkaloids (EA) would accumulate in yellow mealworm larvae (YML, Tenebrio molitor) when present in their diets and investigate effects on production and survival. Larvae were reared on one of four diets: a control, low, medium, and high containing 63, 3,863, 8,471 and 15,316 μg/kg total EA, respectively. Each diet had five replicates with 150 YML per replicate totalling 3,000 for the 21-day trial. Initial and final weights of the feed and larvae were collected. Ergot alkaloid concentrations in YML at d 21 were 32.6, 94.0 and 155.5 μg/kg in the low, medium, and high treatments respectively, with none detected in those fed the control diet. The frass from YML fed the control, low, medium and high diets contained 18, 364, 1,094, and 1,424 μg/kg total EA, respectively. Feed intake was reduced in larvae fed the low, medium and high treatments relative to the control at 23.3-24.9 g/21 d compared to 30.1 g in the control (P=0.02). Feed-to-gain ratios, average daily gain, and final body weights did not differ among treatments (P>0.05). The larvae did not display any preference for diets when allowed to choose between the four diets (P>0.05). Larvae accumulated only low levels of EA from their diets and although feed intake was depressed, growth was maintained. Further research is required to determine the safety of yellow mealworm reared on EA-contaminated diets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".