An investigative study to utilise the Fusarium-damaged wheat as a feedstock for the black soldier fly larvae (Hermetia illucens)
Bibliographic record
Abstract
Abstract This study investigated Fusarium -damaged wheat kernels (FDK) as a potential feeding substrate for black soldier fly larvae ( Hermetia illucens ; BSFL). Fusarium -damaged kernels are considered unsuitable for food and feed due to the presence of mycotoxins. Mycotoxins, like deoxynivalenol (DON), pose health risks when consumed by animals at concentrations exceeding the limits established by the Canadian Food Inspection Agency. However, BSFL have shown higher tolerance to mycotoxins, suggesting that FDK may be used as a feeding substrate for BSFL intended for animal consumption. To assess this, three FDK-based diets with varying concentrations of DON (4.49 ± 0.08 ppm, 6.04 ± 0.02 ppm, and 6.83 ± 0.04 ppm) and a healthy wheat-based diet (0 ppm DON; control diet) were formulated to grow the larvae. The diets were fed to larvae to assess their preference based on DON concentration. Concurrently, the accumulation of DON in BSFL biomass and its effects on growth parameters were evaluated. The larvae showed no preference for any DON concentration. The DON levels accumulated in the BSFL biomass were minimal, regardless of the DON concentration in the feed ( ), with the highest recorded at 0.87 ± 0.04 ppm compared to 6.83 ± 0.06 ppm in the diet. Despite the potential harm of FDK to animals, the growth parameters of BSFL improved, with larvae on FDK-based diets reaching a live body weight of 185.0 ± 3.2 mg compared to 177.6 ± 4.2 mg for the control on Day 15. The nutritional profile remained nearly identical across all DON concentrations (∼41% crude lipid and ∼39% crude protein in dried biomass). These findings suggest that BSFL raised on FDK-based diets can be used effectively for feed purposes.
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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".