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Record W2009106788 · doi:10.1292/jvms.68.113

Relationship between Leukocyte Population and Nutritive Conditions in Dairy Herds with Frequently Appearing Mastitis

2006· article· en· W2009106788 on OpenAlexaff
Hiromichi OHTSUKA, Masayuki Kohiruimaki, Tomohito Hayashi, Ken Katsuda, Keiichi Matsuda, Machiko MASUI, Ryo Abe, Seiichi KAWAMURA

Bibliographic record

VenueJournal of Veterinary Medical Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsHerdMilkingMastitisAnimal scienceBiologyHerd immunityVeterinary medicinePopulationImmunologyMedicineVaccinationMicrobiologyEnvironmental health

Abstract

fetched live from OpenAlex

To clarify the relationship between cellular immune status and nutritive condition, feeding program, blood profiles, and leukocyte populations were analyzed in two dairy herds experiencing frequent mastitis. Fourteen of the 35 lactating cows in herd A, and 18 of the 50 lactating cows in herd B scored positive on the California Mastitis Test (CMT), and 3 of the 73 lactating cows were CMT positive in herd C, which was the control. All herds were evaluated during five different milking stages, and blood was collected from five cows at each stage. With regard to feed content, the percentages of total digestible nutrients (TDN) and crude protein (CP) were found to be lower in herds A and B than in herd C. Levels of serum total cholesterol and blood urea nitrogen were lower in herds A and B than those in herd C. Neutrophil counts in herds A and B were increased compared to the neutrophil counts in herd C. On the other hand, the numbers of CD3(+) T cells and CD14-MHC class(+) cells were lower in herd A and B than in herd C. A decrease in peripheral lymphocytes and undernourishment were observed in the herds with frequent occurring mastitis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.314
Teacher spread0.247 · 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 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

Citations16
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical ScienceSame topicMilk Quality and Mastitis in Dairy CowsFrench-language works237,207