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Record W4406444735 · doi:10.61164/remunom.v1i1.3416

AVALIAÇÃO DA CONTAGEM DE OVOS POR GRAMA DE FEZES (OPG) EM OVINOS E CAPRINOS DA REGIÃO DE PALMAS, TOCANTINS

2025· article· pt· W4406444735 on OpenAlexaff
Eduarda Barros de Pinho, Emily Cristine Da Silva Brito, Jacqueline Seixas dos Santos, Kharenn Gomes Barros, Rayane Dos Santos Gonçalves, Wendy Andrade Meireles, Stérffane Alves Ferreira, Camila Pereira Neres, Clauber Rosanova

Bibliographic record

VenueRevista Multidisciplinar do Nordeste Mineiro · 2025
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiology

Abstract

fetched live from OpenAlex

As verminoses representam um dos principais problemas sanitários da ovinocaprinocultura nacional, sendo as espécies Haemonchus contortus e Trichostrongylus colubriformis os maiores responsáveis pelos prejuízos econômicos da atividade. O diagnóstico do parasitismo animal deve ser embasado em avaliações clínicas e nos resultados dos exames parasitológicos. O grau de infecção parasitária nos animais e a contaminação dos diversos ambientes de produção auxiliam na adoção de medidas de manejo profilático e no tratamento anti-helmíntico adequados a cada sistema de criação, em diferentes ambientes e regiões geográficas. O presente artigo aborda um estudo fundamental sobre o grau de infecção parasitária em rebanhos de ovinos e caprinos de 2 propriedades no município de Palmas, Tocantins. A metodologia utilizada foi a quantitativa de ovos de parasitas por grama de fezes (OPG), uma técnica diagnóstica essencial para identificar infestações por nematódeos que podem comprometer significativamente a produtividade e a saúde animal. A análise microscópica foi realizada na câmara de McMaster, utilizando uma objetiva de 10X para contagem precisa dos ovos de helmintos gastrintestinais. Os resultados obtidos em ambas as propriedades, mostraram infestação significativa, evidenciada pela elevada quantidade de ovos por grama (OPG).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.307
Teacher spread0.272 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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