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The economic impacts of the bovine respiratory disease complex on beef cattle feedlots from Brazil

2025· article· en· W4415109591 on OpenAlexaboutno aff
Anderson Lopes Baptista, Ana Aparecida Correa Xavier, Ramiro Barros Madeira, Hévila Dutra Barbosa de Cerqueira, R. D Gama, Selwyn Arlington Headley, João Paulo Elsen Saut

Bibliographic record

VenueSemina Ciências Agrárias · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBovine respiratory diseaseBeef cattleIncidence (geometry)Mortality rateEconomic impact analysisFeeder cattlePrevalence

Abstract

fetched live from OpenAlex

Bovine respiratory disease (BRD) is a common cause of morbidity and mortality in beef cattle feedlots from countries such as USA, Canada, and Australia. However, data relative to the economic impacts, morbidity, and mortality are scarce in Brazil. This study investigated the incidence of BRD in 10 beef cattle feedlots from different geographical regions of Brazil, during January/2019 to December/2020, with a total of 699,526 cattle on feed. The incidence of general morbidity was 3.6%, while BRD-associated was 2.1%. The incidence of general mortality rate identified was 0.38% and the mortality rate BRD-associated was 0.08%. BRD accounted for 57.9% and 22.1% of all disease cases and deaths, respectively. The average body weight was 14.9 kg higher in cattle without BRD compared to cattle with BRD. The costs of BRD-related mortality and morbidity were estimated at $ 777.98 USD/animal and $ 51.4 USD/animal, respectively, and resulted in an estimated annual loss of $ 6.9 million USD due to morbidity and $ 4 million USD due to mortality. This is the only study in Brazil that investigated the incidence of BRD and the impact on production/economics and the data herein obtained can be used as the starting point to understand the BRD-related losses in the local cattle industry.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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 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
Published2025
Admission routes1
Has abstractyes

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