Bibliographic record
Abstract
A six-year-old Golden Retriever dog with a history of alopecia, itching, scratching and erythema all over the body was presented to Veterinary Teaching Hospital, Nepal Polytechnic Institute, Chitwan, Nepal. Dog weighed 44.6 kg was golden brown in colour. On clinical examination mucous membrane, temperature, Heart rate was normal. The eczematous lesions were erythematous and alopecia was seen in the area surrounding the lesions. The case was suspected of parasitic skin infection, and skin scrapping was taken from different affected area until there was a capillary bleeding with the help of scalpel blade after moistening the skin with glycerin. The scraping was treated with 10% KOH until it was submerged after that the solution was gently heated and the supernatant was discarded, and remnants were transferred to slide. A cover slip was placed, and it was examined first under the low power then high power for detail study. Demodex was observed under microscopic examination. The case was diagnosed as patchy demodex infestation with secondary bacterial infection. The lesions were cleaned by medicated ketachonazole shampoo and systemic administration of fixotic advance and Amoxicillin clavunate 375 mg antibiotic was given for secondary bacterial infection. After 2 weeks skin scrapping test was again performed and found negative. There were no red rashes and lesions were slowly healing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".