The Relationships Among Measures of Egg Albumen Height, pH, and Whipping Volume
Bibliographic record
Abstract
A total of 2123 eggs obtained from Brown Leghorn hens (unselected since 1965, ISA Brown, commercial brown egg layer) and Babcock hens (commercial white egg layer) at 32, 50, and 68 wk of age were used to investigate relationships among measures of albumen quality and a functional property of albumen. The eggs were sampled fresh and after storage for 5 and 10 d. At sampling, eggs were weighed and broken, and albumen height, pH, and volume after whipping for 80 s were measured. Also, yolks were weighed, dried shells were weighed, and albumen weight was determined by difference. Egg weight and the weights of the 3 principal components of the egg all increased with increasing age of the hen, with yolk weights increasing proportionately more. With storage, egg and albumen weights decreased, whereas yolk weight increased. Eggs from Brown Leghorn hens were smallest but had proportionately the largest yolks. Albumen height decreased with time in storage, and albumen pH and whipping volume increased. Differences between lines suggested that selection has changed the proportion of the yolk, albumen, and shell and has increased albumen height. Albumen height and whipping volume were negatively correlated, and differences between lines suggest that selection could have decreased the foaming ability of albumen, a principal reason for including eggs in many processed food products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".