Khảo sát tỷ lệ nhiễm virus gây bệnh tiêu chảy cấp (Porcine epidemic diarhea virus - PEDV) trên heo nái và xác định các yếu tố nguy cơ liên quan đến bệnh PED tại tỉnh Tiền Giang
Bibliographic record
Abstract
Porcine epidemic diarrhea virus (PEDV) is a Coronavirus that caused an enteric infectious disease with high death rate for piglets particularly newborn. A survey on PEDV infection in sows was carried out in Tien Giang province. Blood samples from unvaccinated PED sows were collected. Antibodies against PEDV was determined by ELISA test with Porcine epidemic diarrhea virus antibody test kit, SwinecheckR PED indirect - Biovet – Canada. Results showed that a PEDV prevalence of sows was 33.72%. The highest prevalence was found in Cho Gao district (59.22%), then in Cai Lay (27.66%), Cai Be (14.52%) and lowest rate was in Chau Thanh district (10.20%). The highest PED antibody positive rate was found in the herd of the size from over 50 sows (34.95%). These rates for the herd size of 20-50 sows and under 20 sows were 33.66%, and 31.58% respectively. The positive rate of the sows that have given 4-5 litters and over 5 litters were 56.67% and 38.59% respectively. While these rates for sows given 2 and 3 litters were 33.33% and 27.5% respectively. Analysing the risk factors to PED suspected epidemic showed that, the highest risk factor was not disinfectant housing or disinfecting fewer than one time per every 2 weeks. The others were without disinfectant pits in the house; near distance to the disease outbreaking farm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".