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Histomorphology of sweat glands in different breeds of dogs

2024· article· en· W4406465618 on OpenAlexaboutno aff
K. B. Sumena, K. M. Lucy, N. Ashok, C. Leena, P.V. Tresamol, G. Radhika

Bibliographic record

VenueJournal of Veterinary and Animal Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsSWEATBiologyZoologyAnatomyPaleontology

Abstract

fetched live from OpenAlex

This study examined the histomorphology of sweat glands across various dog breeds, focusing on their structural characteristics and adaptations to environmental needs. Skin samples from 48 dogs representing indigenous and seven breeds (German Shepherd, Labrador Retriever, Dachshund, Beagle, Doberman Pinscher, German Spitz and Pug) were collected from the ventral abdominal region. Using standard histological techniques, histology and the size and distribution of sweat glands were analysed. The results revealed significant variations in gland size and number among different breeds. Breeds like the German Spitz, German Shepherd and Dachshund had the largest sweat gland diameters, suggesting a better capacity for thermoregulation. In contrast, the Pug, Doberman Pinscher and Labrador Retriever had fewer and smaller glands. Dachshund stood out for possessing both the highest number and large-sized glands, while German Spitz had fewer but larger glands, compensating for the lesser number. The types of sweat glands predominating in ventral abdominal regions in dogs were apocrine glands. It was concluded that significant differences existed in the number and size of the sweat glands among different breeds which play a key role in thermoregulation. Keywords: Sweat glands, dogs, breed comparison

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.425
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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