Field evaluation of CIEP and PCR detection/removal control methods of Aleutian mink disease (AD) in Canada
Bibliographic record
Abstract
Detection/removal control method of Aleutian disease by CIEP detection of ADV-antibody and farm or barn depopulation/repopulation, have been the recommended approaches of AD control since mid 1970s. The detection/removal was, at least under common N. American husbandry conditions, unsuccessful in controlling AD in a sustainable manner. Recently, attention was turned towards virus detection by PCR, and its use for AD eradication by removal of positive individual animals. In view of the common failures of CIEP to facilitate AD eradication, we were skeptical about premature acceptance of PCR-detection/removal, as the recommended control method. The frequent failures of CIEP test/removal were often blamed on breaches of bio-security. However, we believed that this method has been based on fundamentally wrong premise that the virus is primarily harbored by the infected animals. In reality, this sturdy parvovirus is harbored primarily in the contaminated environment through feces, saliva, urine, whelping, as well as through blood during bleeding for testing. While the use of both CIEP and PCR for monitoring of farms free of the virus remains certainly a valid approach, the data obtained in this study indicate that detection/removal by neither of the methods could facilitate real and lasting freedom from the virus, under the conditions of the study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".