Fatal Sarcoptes scabiei Infection of Blue Sheep (Pseudois nayaur) in Pakistan
Bibliographic record
Abstract
Sarcoptes scabiei was detected for the first time in skin scrapings, hair pluckings, and histologic sections from a blue sheep (Pseudois nayaur) from the Shimshali Pamir in the Karakorum range of the western Himalaya in Pakistan (36 degrees 28'N, 75 degrees 36'E). Local reports suggest many hundred animals have been affected by a severe skin disease over a 10-yr period, but the shy nature of this species and the extreme climate that they inhabit meant only a single affected animal was available for detailed evaluation. The severe skin lesions were confined to the forelegs and brisket, and many Sarcoptes scabiei mites were present in all the samples examined. Histologic preparations of the skin showed hyperkeratotic and parakeratotic hyperkeratosis of the epidermis with a severe exudative dermatosis with many polymorphonuclear neutrophils and gram-positive cocci, yet no eosinophils. These findings might suggest the lack of an appropriate immune response to the parasite or other coping strategies because there has been no abatement of the clinical signs in affected animals over several years. Treatment options are limited due to the behavior of the species and its habitat. The blue sheep is a primary source of prey for the endangered snow leopard (Panthera uncia) and continued depletion could have serious consequences for the survival of the latter.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".