Analysis of Skin Microbiota from Canine Dermatitis in Odisha
Bibliographic record
Abstract
In the present study 86 number of skin scrapings were collected from various breeds like Labrador retriever (n=32), German shepherd (n=21), Spitz (n=9), Dacshund (n=16), Mastiff (n=5), Boxer (n=3) and age groups from 6 months to, above 3 year with characteristics ring like lesions, alopecia, irregular patches of hair loss. All the dogs were presented to teaching veterinary clinical complex, Odisha veterinary college from December 2018 to June 2019.On routine identification of fungus out of 86 number of skin scrappings it was revealed that arthospores were present in 54 number of samples. The predominant fungal infection from the ringworm infected dog cases was found to be Microsporum species (48.15%). Microscopic examination with cotton blue stain and wet mount method by 10% aqueous solutions of potassium hydroxide was done for identification of dermatophytes. Highest percent of infection was noticed between 6month to one and half year aged groups of canine species, may be due to poor development of immunity. Highest prevalence of dermatomycosis was recorded in Labrador retriever (37.03%) followed by German shepherd breed (25.92%) of canine species. On disk diffusion method of antifungal susceptibility test it was found that Miconazole (10µg) was found to be highly sensitive followed by Ketoconazole (15µg), Clotrimazole (10µg).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".