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Record W4285202529 · doi:10.31893/jabb.22021

Circus Animal Welfare: analysis through a five-domain approach

2022· article· en· W4285202529 on OpenAlexaff
Daniel Mota‐Rojas, Marcelo Daniel Ghezzi, Adriana Domínguez-Oliva, Leonardo Thielo de La Vega, Luciano Boscato-Funes, Fabiola Torres-Bernal, Patricia Mora‐Medina

Bibliographic record

VenueJournal of Animal Behaviour and Biometeorology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsAnimal welfareWelfareAffect (linguistics)Perspective (graphical)Mental healthPsychologyMental stateQuality (philosophy)SociologyApplied psychologyPolitical sciencePsychiatryComputer scienceCommunicationLawEcologyBiologyEpistemology

Abstract

fetched live from OpenAlex

This study aims to review the current available literature regarding circus animals from the perspective of the five domains proposed for evaluating animal welfare to identify the critical points in the use of these animals and understand how circus spectacles affect their mental state and health. Exhibiting animals in circuses continues to be a popular practice today in some countries such as Germany, Spain, or Australia. However, animals’ biological needs are not always prioritized due to the inadequate diets, reduced housing spaces, deficient social interaction, and handling that predisposes them to develop stereotypies and alter mental states due to chronic stress. Animal circuses are considered a controversial practice that can decrease the welfare of animals. Understanding the possible negative consequences on animal welfare (mental state and physical health) could contribute to planning strategies to improve the quality of life of wildlife animals exhibited in circuses worldwide.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0080.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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