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Record W4401948785 · doi:10.51161/convesp2024/39834

OCORRÊNCIA DE CONTUSÕES E PERDAS ECONÔMICAS DE CARCAÇAS BOVINAS ABATIDAS EM ABATEDOURO-FRIGORÍFICO COM SERVIÇO DE INSPEÇÃO VETERINÁRIA NO ESTADO DE RORAIMA

2024· article· pt· W4401948785 on OpenAlexaff
Samara Helen Carvalhedo Boaes, JOICY COMPAGNON-MARIANO, MARIANA ALEXANDRE LOBO, André Buzutti de Siqueira, Everton Ferreira Lima

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsBiology

Abstract

fetched live from OpenAlex

23,5%) e grau III (7,6%).As leses foram mais comuns no coxo (45,8%), lombo (37,6%), gradil costal (9,7%) e dianteiro (6,9%).Embora no tenha sido possvel calcular os prejuzos econmicos diretamente, leses em carcaas causam perdas significativas tanto para produtores quanto para frigorficos.A concluso aponta para falhas no manejo do bem-estar animal durante o embarque, transporte e desembarque, sugerindo a necessidade de melhorias nos programas de bem-estar animal para reduzir prejuzos econmicos e melhorar a qualidade da carne, consolidando a cadeia produtiva brasileira no mercado mundial.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.001

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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

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