Effect of feeding broccoli floret residues on leghorn layer performance and egg quality and nutrient digestibility
Bibliographic record
Abstract
1. A study was conducted to determine the effects of dried broccoli floret (DBF) on egg production parameters, egg quality and apparent nutrient digestibility of laying hens. It was hypothesised that feeding DBF can improve egg quality without adversely affecting layer performance.2. Seventy-two White Leghorn layers aged 62 weeks were randomly allotted to 4 dietary treatments (6 cage replicates with 3 hens each) and fed diets containing 0, 40, 80 and 120 g/kg DBF in feed for 56 d.3. Inclusion of DBF had no effect on feed intake, egg production, feed conversion ratio or egg components. However, egg weight increased (linear effect, P < 0.05) as the level of DBF in the diet increased. Egg breakage strength, eggshell strength and Haugh unit were not influenced by dietary treatments. Egg yolk colour (quadratic effect, P = 0.002) and α-tocopherol concentration (quadratic effect, P < 0.0001) improved as a result of DBF inclusion. Egg yolk cholesterol concentration was not altered by DBF inclusion. Apparent total tract nutrient (dry matter, organic matter and crude protein) digestibility coefficients decreased (linear effect, P < 0.05) as the level of DBF increased.4. Feeding DBF to layers up to 120 g/kg of the diet had no negative effects on feed intake, egg production and feed efficiency and improved egg yolk colour and α-tocopherol concentration. However, total tract nutrient digestibility coefficients were negatively decreased as a result of DBF inclusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".