A COMPARATIVE STUDY ON THE SKIN OF DIFFERENT BREEDS OF DOGS
Bibliographic record
Abstract
A comparative study was conducted on the skin samples from different breeds of dogs, focusing on epidermal, dermal and total skin thickness, hair distribution pattern, subcutaneous fat and sebaceous as well as sweat gland characteristics.Notable variations were observed among breeds.Skin was thickest in Doberman Pinscher and thinnest in Beagle.The Labrador Retriever showed the highest epidermal contribution to total skin thickness (7.06%).Dachshund and Beagle exhibited the thickest (85.07±5.97µm) and thinnest (29.30±2.98 µm) epidermis.Significant positive correlation was noticed between total skin thickness and dermal thickness.A significant negative correlation was observed between epidermal thickness and both dermal and total skin thickness.Hair distribution was compound in pattern in all breeds but varied in number and size across breeds.The primary hair and many secondary hairs emerged through a single opening.Among the eight breeds under study, maximum diameter for hair was noticed in Doberman Pinscher and minimum in the German Spitz.Maximum number of sweat glands was observed in the Dachshund and minimum in the Pug.The sweat glands in the ventral abdominal region in all breeds were of apocrine type.Simple branched alveolar type holocrine sebaceous glands associated with the hair follicles were present in dermis in all the breeds.These findings highlight breedspecific histological differences in canine skin that may influence their physiological and dermatological traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".