Comparative Suitability of Ear Notch Biopsy and Serum Pairs for Detecting Nature of Bovine Viral Diarrhoea Virus Infection in Dairy Herds
Bibliographic record
Abstract
Suitability of ear notch biopsy (EN) and serum pairs (n= 307) collected from 10 Holstein dairy herds located in Charlottetown, Canada was evaluated for simultaneous detection, nature of bovine viral diarrhea virus (BVDV) infection and genotype of the prevailing BVDV through Real time RT-PCR. Depending upon vaccination status and age, the sampled animals were categorized into two groups, A (n=123, ≤ 6 month of age) and B (n=184, ≥ 6 months of age) originating from 4 vaccinated (n=108) and 3 non-vaccinated (n=76) animal herds. On first round of testing a discrepancy between ear notch biopsies and sera pairs (3.25 and 6.50%; P<0.05) of groups A was observed, however, a complete harmony (50% for EN and sera each, P<0.01 was found on second round of testing that confirmed the presence of 4 persistent infection (PI) animals harboring genotype 1 of BVDV. Complete concordance between EN and sera pairs (P<0.01) on first and follow up testing in group B was observed (2.77%, each), depicting 3 PI animals with the same genotype as in group A. In the study, ear notch biopsies did not detect any transient infection (TI) but sera samples detected 3.25% transiently infected animals in group A that was 1.30 % among all the test samples (n=307) while no TI animal was found in group B. It may be concluded that both the serum and ear notch biopsy can be used to detect PI animals and that, serum samples are more sensitive than ear notch (P < 0.05) for detection of TI using real time RT-PCR.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".