Prediction of Optimal Vaccination Timing for Infectious Bursal Disease Based on Chick Weight
Bibliographic record
Abstract
Growth rate in broiler birds has increased substantially in the last decade due to improvement in genetics, feed formulation, cleaner environment, and vaccine formulations. As a result, it has become necessary to review and revise prediction method for vaccination in chicks. This study was undertaken to determine the possible use of the rate of weight gain rather than age in predicting vaccination time. Two groups of 1-day-old broilers originating from old and young breeders, respectively, and with different levels of maternal antibodies against infectious bursal disease virus (IBDV) were used in this study. The chicks were divided into four groups and subjected to two feed regiments: groups A1 and B1 were fed broiler feed for normal growth rate, and groups A2 and B2 were fed breeder feed for slower growth rate. At 1, 4, 8, 12, 16, 22, 29, and 36 days of age, 22 chicks in each group were weighed, and blood samples were collected. Serum samples were tested for antibodies against IBDV by enzyme-linked immunosorbent assay (ELISA) and virus neutralization test. Maternal antibody decline curves for each group were plotted according to chick age and chick weight. Fast-growing birds in groups A1 and B1 showed a faster rate of antibody decline, whereas slow-growing birds in groups A2 and B2 had a slower rate of antibody decline. Based on the effect of weight gain on maternal antibody decline, a new way of predicting vaccination time for IBDV based on measuring maternal antibody titers at 4 days of age was proposed and tested. The predicted antibody decline was shown to correspond to the real ELISA titers measured in our experiments (R = 0.9889), whereas a lower correlation (R = 0.8355) was detected between real ELISA titers and the titers predicted by the current method using age-based Deventer formula.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".