Feeding laying hens with insect meal affects the production traits and some quality parameters of table eggs
Bibliographic record
Abstract
A feeding trial was carried out with Tetra SL laying hens to evaluate the effects of an insect larvae meal on the production traits and egg quality parameters. Alphitobius diaperinus larvae meal (ADM) was incorporated at 10% (ADM10) and 15% (ADM15) into layer diets on the expense of soybean meal and the production traits and egg quality parameters, have been evaluated. Among the production traits only egg weight was affected by dietary treatments. Both ADM diets decreased egg weight in comparison with the control diet. Besides egg weight, none of the other egg quality parameters were affected. ADM failed to modify the fat content of egg yolk but resulted significant changes in the fatty acid composition of the yolk fat. Feeding ADM increased the lauric, stearic and linoleic acid and decreased the oleic and α-linolenic acid concentrations of the yolk. The results of the electronic nose proved that ADM resulted in specific volatile compound structure, that can be used for the identification of the insect meal eggs. Among the identified compounds ADM reduced the intensity of dimethyl sulphide, ethyl acetate and acetaldehyde in comparison with the control eggs. The tasting and organoleptic evaluation of boiled eggs by the volunteers resulted higher quality scores and acceptance of the ADM eggs. From the results it can be concluded that insect meals can be used efficiently even at high incorporation rates in layer diets. The reason for the egg weight reduction, however, needs further investigation.
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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 | 0.001 |
| 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".