Bibliographic record
Abstract
In view of free from bluetongue (BT) in the domestic cattle population in Korea, the key of quarantine testing for BT virus (BTV) infection is detection of cattle previously exposed to the virus. The objective of this study was to estimate the probability of detecting a cattle infected with BTV using a stochastic modeling analysis of existing quarantine testing data. Three testing scenarios were considered in this study: serological testing of all animals in all imported lots (scenario 1), serological testing of a sample of cattle from all imported lots (scenario 2), and serological testing of 50% of imported lots (scenario 3). In scenario 2 and 3, it was assumed that cattle were sampled (sample size) within each lot to detect 5% of the cattle in each lot with a 95% confidence, taking into account diagnostic sensitivity of the ELISA (enzyme-linked immunosorbent assay). The model output was the total number of BTV-infected cattle and the prevalence of BTV infection in imported cattle from the US, Australia, Canada and Japan. Compared to the scenario 1, the probability of detecting a BTV-infected cattle was estimated to be 19% and 1.6% in scenario 2 and 3, respectively. Furthermore, the analyses showed a 95% confidence that BTV prevalence was less or equal to 9.7 × 10?⁴ (median = 1.5 × 10??), indicating that, for the scenario 2 and 3 with serological testing for a sample of cattle, the risk of introducing an exotic strain of BTV into Korea through the importation of live cattle would not be acceptable.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".