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Record W2964515729 · doi:10.1556/019.70.2019.17

Social behavior in dog puppies: Breed differences and the effect of rearing conditions

2019· article· en· W2964515729 on OpenAlexaboutno aff
Rita Lenkei, Ákos Pogány, Claudia Fugazza

Bibliographic record

VenueBiologia Futura · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersEmberi Eroforrások MinisztériumaMagyar Tudományos Akadémia
KeywordsBreedAnimal behaviorSocial behaviourBiologyPsychologyDevelopmental psychologyAnimal scienceZoology

Abstract

fetched live from OpenAlex

INTRODUCTION: Most of the studies investigating the effect of early rearing environment in dogs used laboratory dogs and reported that early experiences markedly affect the puppies' behavior. However, the subjects of these experiments cannot be considered as representatives of family dogs. METHODS: In this study, we investigated whether different raising conditions shape social behavior toward humans in 8-week-old family dog puppies of two breeds, Labrador and Czechoslovakian wolf dog. The puppies were tested in a series of tests that represented typical situations of family dogs. RESULTS: We found that Czechoslovakian wolf dog puppies were more active than Labrador puppies in general, as they were more likely to explore the environment and the objects and spent more time doing so. Tendency to gaze at humans also varied between breeds, but in a context-specific way. Additionally, puppies housed separately from their mother interacted more with toys, puppies housed in a kennel tended to stay closer to the experimenter than puppies raised in the house, and puppies housed in a kennel tended to stay in the proximity of the experimenter more than puppies raised in the house. CONCLUSIONS: Our results provide evidence for early keeping conditions influencing social behavior and also highlight breed differences in puppies' behavior. Whether these differences are due to different developmental patterns and/or behavioral predispositions remains to be explored.

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 score0.424
Threshold uncertainty score0.195

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 head0.336
Teacher spread0.322 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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