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A INCIDÊNCIA DE MASTITE EM REBANHOS DE VACAS LEITEIRAS

2023· article· pt· W4386926146 on OpenAlexaff
Eduardo Cezar Gutter, Leonardo Campostrini Favarato, Vinicius Lopes Ferreira

Bibliographic record

VenueRevista Foco · 2023
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHerdGeographyVeterinary medicineAnimal scienceBiologyMedicine

Abstract

fetched live from OpenAlex

O presente estudo teve como objetivo avaliar a incidência da mastite bovina subclínica e clínica, em dois rebanhos leiteiros situados nos municípios de Colatina e João Neiva. Para tal, foram avaliadas 160 amostras de leites provenientes de propriedades utilizadoras de ordenha manual e mecanizada. Onde foi realizado o teste de caneca de fundo preto e o teste de CMT como métodos de diagnóstico da mastite. Na propriedade com ordenha manual, 27,5% das amostras testaram positivos para mastite clínica e subclínica, já na propriedade com ordenha mecanizada, 31,25% testaram positivo para a doença. Conclui-se que as duas propriedades apresentam alta taxa de contaminação em seus rebanhos, como consequência de falhas na execução no manejo de ordenha e nas medidas preventivas como higienização de tetos, pré-dipping e pós-dipping.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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