An appraisal of moulting on post-moult egg production and egg weight distribution in white layer hens; meta-analysis
Bibliographic record
Abstract
1. The aim of this study was to establish how different moulting methods and body weight losses influenced post-moult performance and USDA egg weight distribution. 2. Data on 5 laying flocks (#34-38) of the North Carolina Layer Performance and Management Test were used in this meta-analysis. 3. The moulting methods were non-fasted moulted (NF), short feed restricted (SF), 13-d feed restricted (FR), non-anorexic moult programme (NA), non-anorexic moult programme with low sodium (NALS) as well as non-moulting programme as control treatment. The percentages of targeted body weight loss during the moulting period were 20, 24, 25 and 30% of body weight at the end of the first egg production cycle. 4. Post-moult egg production and egg mass were influenced by all moulting methods. Maximum increase in post-moult egg production rate and egg mass occurred with FR and NF programmes, respectively, at 30% of body weight loss, compared to non-moulted hens. Non-fasting methods reduced mortality rate more effectively than fasting methods. 5. Moulting resulted in increases in percentage of grade A and decreases in percentage of grade B eggs. Non-fasting methods increased percentage of grade A eggs more effectively than fasting methods. Percentage of cracked eggs decreased in moulted rather than non-moulted hens and the lowest rate was associated with the NA programme. 6. Post-moult egg weight was not significantly influenced by moulting methods. However, percentage of body weight reduction affected egg weight. The optimum increment in egg weight was achieved by 24% body weight reduction. 7. Overall, non-fasting methods resulted in similar egg production compared with fasting methods. Considering post-moult mortality and USDA egg weight distribution, non-fasting methods, especially NF and NA programmes, performed much better than fasting methods, indicating that non-fasting moulting methods, which are better for animal welfare, are effective alternatives to fasting methods.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".