Experimental oral transmission of chronic wasting disease to sika deer (<i>Cervus nippon</i>)
Bibliographic record
Abstract
Chronic wasting disease (CWD) affects a broad array of cervid species and continues to be detected in an expanding geographic range. Initially introduced into the Republic of Korea through the importation of CWD-infected elk (Cervus canadensis), additional cases of CWD were subsequently detected in farmed Korean elk and sika deer (Cervus nippon). Wild and farmed sika deer are found in many regions of Asia, North America, and Europe, although natural transmission to this species has not been detected outside of the Republic of Korea. In this study, the oral transmission of CWD to sika deer was investigated using material from CWD-affected elk. Pathological prion (PrPCWD) immunoreactivity was detected in oropharyngeal lymphoid tissues of one sika deer at 3.9 months post-inoculation (mpi) and was more widely distributed in a second sika deer examined at 10.9 mpi. The remaining four sika deer progressed to clinical disease between 21 and 24 mpi. Analysis of PrPCWD tissue distribution in clinical sika deer revealed widespread deposition in central and peripheral nervous systems, lymphoreticular tissues, and the gastrointestinal tract. Prion protein gene (PRNP) sequences of these sika deer were identical and consistent with those reported in natural sika deer populations. These findings demonstrate the efficient oral transmission of CWD from elk to sika deer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".