MétaCan
Menu
Back to cohort
Record W4310802644 · doi:10.5604/01.3001.0016.1242

Dependencies between animal and environmental factors and behavioral problems in horses: a survey-based analysis

2022· article· en· W4310802644 on OpenAlexaboutno aff
M. Budzyńska, Wiktoria Janicka, Martyna Michalska, Joanna Kapustka

Bibliographic record

VenueANIMAL SCIENCE AND GENETICS · 2022
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareTemperamentWelfarePsychologyDevelopmental psychologySocial psychologyBiologyEcologyPersonality

Abstract

fetched live from OpenAlex

Undesirable and stereotypical behaviors may indicate abnormalities in the living environment of animals. They have a negative impact on the welfare of animals and hinder the handling and use thereof. Unfortunately, horse owners often fail to recognize the causes of behavioral problems in their animals. The aim of the study was to analyze the breeding conditions, type of use and individual traits of horses in relation to displaying undesirable and stereotypical behavior. The assessment was made based on 609 online questionnaires completed by horse owners from Poland (n=376), UK (n=145), USA and Canada (n=66), Australia and NZ (n=22). It has been shown that the undesirable behaviors are influenced by e.g. access to pastures, presence of other horses on paddocks and supply frequency of roughage. Problematic behaviors are particularly frequently displayed by animals with a lively temperament. Horses should be kept under conditions meeting their feeding, social and locomotor behavioral needs. The evaluation of their individual traits may help to assess the risk of developing abnormal behavior and thus, to prevent it. Raising the awareness of horse keepers and breeders of the causes of problem behaviors should be the key issue.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.157
GPT teacher head0.385
Teacher spread0.228 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueANIMAL SCIENCE AND GENETICSSame topicVeterinary Equine Medical ResearchFrench-language works237,207