Egg production, egg quality, and fatty acids profiles in eggs and tissues in Lohmann LSL lite hens fed algal oils rich in docosahexaenoic acid (DHA)
Bibliographic record
Abstract
Enriching eggs with omega-3 fatty acids (n-3 FA), such as docosahexaenoic acid (DHA), is a well-accepted practice that benefits the egg industry and consumers. However, issues around cost, sustainability, and product acceptance have necessitated the search for alternatives to feeding hens fish oil for DHA enrichment. The effects of feeding 2 algal oils on egg production and DHA enrichment in eggs and selected tissues were investigated. The algal oils were: 1) OmegaPro (OPAO) standardized algal oil for DHA content and 2) Crude algal oil (CAO). A total of 400, 46-wk-old Lohmann LSL lite hens were housed in enriched cages (10 birds/cage) and allocated 5 diets (n = 8) for a 12-wk trial. The iso-caloric and -nitrogenous diets were a standard corn and soybean meal diet, standard plus 0.25 or 0.76% OPAO and standard plus 0.23 or 0.69% CAO; algal oils diets supplied similar DHA at each level. Egg production indices (hen day egg production, feed intake, FCR, egg weight, egg mass, and eggshell quality) were monitored for 10 wk. Diet samples were analyzed for fatty acids (FA) on wk 1, 6, and 12 and eggs on wk 4, 5, 6, 9, and 12. At the end of the trial, one hen/cage was weighed and dissected for liver, breast and thigh for FA and long bones for ash content analyses. Concentration of omega-6 to omega-3 FA ratio was 12.9, 6.64, 3.48, 6.96, and 3.59 for standard, 0.23 and 0.76% OPAO, 0.25 and 0.69% CAO, respectively. Algal oils increased (P ≤ 0.046) eggshell thickness linearly. The concentration of DHA in the eggs from the birds fed the standard, 0.23 and 0.76% OPAO, 0.25 and 0.69% CAO was 84, 195, 286, 183, and 297 mg/100g egg, respectively, and algal oils enriched eggs with DHA linearly and quadratically (P ≤ 0.01). In conclusion, algal oils increased the concentration of DHA in eggs and had no adverse effects on egg production and eggshell quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".