Indirect Method for Prediction of Hemagglutination Inhibition Antibody Titers to Newcastle Disease Virus in Chickens by Titration of Antibodies in Egg Yolk
Bibliographic record
Abstract
Attempts were made to establish methods for indirect prediction of hemagglutination inhibition (HI) antibody titers to Newcastle disease virus (NDV) in sera of laying hens and day-old chicks by determining if these are correlated to HI titers in egg yolks. For this purpose, geometric means of HI antibody titers in sera from 60 hens, yolks from 60 matched eggs, and sera from 180 day-old chicks of an identical vaccination program were measured and plotted. There was a significant correlation between HI antibody titers in yolks (X) and hens (Y), with a linear regression of Y = 23.24 + 0.47X and a correlation coefficient of r = 0.65. The linear regression between HI antibody titers in yolks (X) and chicks (Y) was Y = 6.33 + 0.36X (r = 0.58). Immunity to NDV in hens and their offspring can be maintained effectively, and the proper time for the vaccination or booster can be determined by reference to HI titers predicted from the linear regression in the present study. The approach of testing egg yolk for HI titers provides a feasible alternative to determining HI titers from blood samples and eliminates stress in birds during blood sampling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".