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Record W3170820706 · doi:10.33448/rsd-v10i6.15996

Ação de capacitação de boas práticas de manejo e bem-estar animal aos profissionais do Turfe do Jockey Club de Pelotas

2021· article· pt· W3170820706 on OpenAlexaff
Taís Scheffer Del Pino, Bruna da Rosa Curcio, Giovana Mancilla Pivato, Margarida Aires da Silva, Gabriela Castro da Silva, Ruth Patten, Carlos Eduardo Wayne Nogueira

Bibliographic record

VenueResearch Society and Development · 2021
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsClubHumanitiesBiologyPhilosophyAnatomy

Abstract

fetched live from OpenAlex

Este estudo traz a importância de ações educativas constantes aos profissionais do turfe do Jockey Club de Pelotas (JCP). O projeto de extensão “ClinEq - Grupo de Ensino, Pesquisa e Extensão em Medicina de Equino da Faculdade de Veterinária da UFPEL em parceria com o JCP realizou capacitação profissional com o objetivo de oferecer conhecimento e avaliar a percepção dos profissionais sobre boas-práticas de manejo com cavalos de corrida. Foram realizados seis encontros com exposição dos principais temas de manejo e três questionários referente a percepção dos treinadores em relação ao assunto abordado. Observou-se durante as capacitações que os profissionais demonstraram grande interesse nos conteúdos e aproveitaram todos os tópicos abordados, entendendo como um ganho na sua atuação profissional e melhorando seu entendimento sobre os conteúdos. Conseguindo durante o período que transcorreu a capacitação, colocar em prática algumas das sugestões apresentadas para melhorar as boas-práticas de manejo com os cavalos no JCP.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.079
GPT teacher head0.337
Teacher spread0.258 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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