Effect of Storage and Layer Age on Quality of Eggs From Two Lines of Hens
Bibliographic record
Abstract
Eggs from ISA-White and ISA-Brown hens between 28 and 59 wk of age were stored for up to 10 d to produce a sample of 5,763 eggs differing in the three major determinants of albumen quality. Eggs from ISA-Brown hens were larger and had less yolk, more albumen, and a greater percentage of shell than those from ISA-White hens. Egg size increased with increasing age of the hen, although more for the ISA-White hens than the ISA-Brown hens, and the yolk increased more in size than did the shell and albumen. During storage, albumen weight decreased and yolk weight increased slightly. The height of the inner thick albumen of eggs from ISA-White hens was greater than that of eggs from ISA-Brown hens, and it decreased as the hen age increased and with increasing time in storage. The pH of the albumen was not different between strains, and the effect of hen age was small, but it increased with time in storage. Regression coefficients of the height of the inner thick albumen on the weight of the egg were between -0.058 and 0.102, showing that the fixed regression of 0.05-mm albumen height per gram of egg implied by the Haugh unit is wrong. The statistical association between albumen pH and egg weight was very low. If albumen quality is being used as a measure of freshness, then the albumen height is biased by the strain and age of hen, whereas the albumen pH is not.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".