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Hair cycle in dogs with different hair types in a tropical region of Brazil

2007· article· en· W2123006798 on OpenAlexaboutno aff
Evandro Silva Favarato, Lissandro Gonçalves Conceição

Bibliographic record

VenueVeterinary Dermatology · 2007
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsHair cycleBreedHair follicleBiologyphotoperiodismAnimal scienceVeterinary medicinePhysiologyMedicineEndocrinologyBotany

Abstract

fetched live from OpenAlex

Hair cycle activity has been extensively studied in humans, sheep and laboratory animals, but there is a lack of information in dogs. Besides varying according to species, breed, sex and general health, hair growth is mainly affected by climatic variations. The aim of the study was to evaluate the follicle activity in three breeds of dogs with different hair types, in the city of Viçosa, Minas Gerais (latitude 20 degrees 45'S), Brazil. Twenty-one male dogs of boxer, labrador and schnauzer breeds were trichographically analysed monthly over 12 consecutive months. Hair percentage of telogen and anagen hairs at the different stages of the hair cycle in boxers and labradors was not significantly different, but both differed from the schnauzers. A significant correlation between hair follicle cycle and environmental temperature and photoperiod was noted in the boxers and labradors. In these breeds, a larger number of telogen hairs were observed during the hottest months of the year, and an increase in anagen hairs during the coldest months. The mean percentage of telogen hairs was 93, 90 and 55.3% for boxer, labrador and schnauzer, respectively.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.280
Teacher spread0.265 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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