Improving duckling hatchability and quality by optimization of egg turning angle during incubation
Bibliographic record
Abstract
Egg turning in incubation is crucial to the development of embryos and hatching performance. We aimed to develop a high performance duck egg incubation technique by enlarging and changing egg turning angles. Increasing turning angle from 45 to 75° did not affect the embryo early mortality during the first 15 d of incubation, which ranged from 3.5 to 4.0%, but accelerated chorioallantoic membrane (CAM) development by 17 h, and significantly (P < 0.01) reduced the late mortality from 9.4 ± 0.98% to 5.31 ± 0.63%. As the result, fertile egg hatchability increased from 91.03 ± 0.97% to 94.64 ± 0.61% (P < 0.05), so was healthy duckling rate from 87.24 ± 1.17% to 92.08 ± 0.55% (P < 0.05), and duckling live weight from 60.74 ± 0.63 g to 63.15 ± 0.35 g (P < 0.05). Changing turning angle from 75°to 60°during incubation d 15 to 25 further reduced late embryo mortality to 3.88 ± 0.47 and increased hatchability to 96.58 ± 0.68%. This changing angle turning hatched ducklings exhibited the highest growth performance during rearing than those hatched by 45 and 75° egg turning. The enhanced growth rate was paralleled by upregulations of somatotropic axis genes mRNA expression levels of the hypothalamus GHRH, liver GHR and IGF-1 during embryo incubation and duckling rearing. In conclusion, a changing angle egg turning incubation technique, 75°in the first 15 d and 60°thereafter, can enhance CAM development, upregulate somatotropic axis genes expressions, and can maximally improve embryo livability, duckling hatchability and growth performance.
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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".