Evaluating <i>Brassica napus</i> and <i>Brassica juncea</i> meals with supplemental enzymes for use in brown-egg laying hen diets: production performance and egg quality factors
Bibliographic record
Abstract
Canola and juncea meals (CM and JM) have been thoroughly evaluated in diets of white- but not brown-shell egg laying hens (BSLH). This study compared the effects of dietary CM, JM, or soybean meal (SBM) on production performance and egg quality of BSLH. Over 48 wk, 300 Lohmann Brown-Lite laying hens were fed diets containing SBM, 10% or 20% CM (CM-10 or CM-20), and 10% or 20% JM (JM-10 or JM-20), without (−E) or with (+E) a phytase/multicarbohydrase enzyme cocktail. Egg weight decreased with CM-20 inclusion compared with SBM (P = 0.027; SBM, 63.1a; CM-10, 61.8ab; CM-20, 61.1b; JM-10, 62.6ab; JM-20, 61.7ab; g egg−1). In a meal by enzyme interaction, enzyme inclusion decreased percent shell and egg specific gravity of only the hens fed CM-20 (P ≤ 0.008). Body weight decreased (P = 0.031; −E, 2135a; +E, 2078b; g hen−1) and feed efficiency was improved (P = 0.032; −E, 1.98a; +E, 1.95b; g feed g egg mass−1) when enzyme was included in the diet. Dietary treatment did not affect mortality (P > 0.05). All performance and quality parameters were within expected ranges; therefore, 20% CM and JM can be included in BSLH diets, and enzyme inclusion can be used to improve feed efficiency regardless of meal type fed.
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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".