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Contagem de células somáticas em amostras de leite

2004· article· pt· W2127143268 on OpenAlexaff
Meiby Carneiro de Paula, Newton Pöhl Ribas, H.G. Monardes, Júlio Eduardo Arce, Uriel Vinícius Cotarelli de Andrade

Bibliographic record

VenueRevista Brasileira de Zootecnia · 2004
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyAnimal science

Abstract

fetched live from OpenAlex

Objetivou-se neste trabalho avaliar os fatores que influenciam a contagem de células somáticas (CCS) em amostras de leite de tanques e analisar a distribuição destas amostras nas classes de escore de células somáticas (ECS). Foram analisadas 257.540 amostras de leite de tanques, provenientes de 32.590 rebanhos de 18 indústrias de laticínios, localizadas nos Estados de Santa Catarina, Paraná e São Paulo, associadas ao Programa de Análise de Rebanhos Leiteiros do Paraná (PARLPR) da Associação Paranaense de Criadores de Bovinos da Raça Holandesa (APCBRH), no período de janeiro de 1999 a novembro de 2001. Empregou-se o procedimento PROC GLM, do SAS, para o estudo dos seguintes efeitos: micro-região, ano e mês de análise, idade da amostra e rebanho. A média e o desvio-padrão amostral para a CCS foram de 486.812 e 401.547 células/mL, respectivamente. Todos os efeitos incluídos no modelo foram significativos sobre a CCS. Houve grande variação da CCS entre as micro-regiões, sendo a maior e menor médias ajustadas para CCS de 602.000 e 242.000 células/mL, respectivamente. No ano de 2001, foi observada a maior média para a CCS (483.000 células/mL). A maior média de CCS foi observada no mês de janeiro (497.000 células/mL) e a menor no mês de setembro (442.000 células/mL). O efeito de idade da amostra mostrou redução da CCS até o quarto dia e, a partir do sétimo dia, as médias sofreram grandes variações. Das amostras analisadas, 64,6% apresentaram escore cinco ou maior.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.277
Teacher spread0.245 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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