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Record W3155922183 · doi:10.3389/fvets.2021.658031

Non-aureus Staphylococci and Bovine Udder Health: Current Understanding and Knowledge Gaps

2021· review· en· W3155922183 on OpenAlexaff
Jeroen De Buck, Vivian Ha, Sohail Naushad, Diego B. Nóbrega, Christopher D. Luby, John R. Middleton, Sarne De Vliegher, Herman W. Barkema

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsCanadian Food Inspection AgencyUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsUdderMastitisBiologyMicrobiomeVirulenceStaphylococcus aureusMicrobiologyVeterinary medicineBacteriaMedicineGenetics

Abstract

fetched live from OpenAlex

staphylococci (NAS) in udder health are not entirely understood. These Gram-positive bacteria have become the most frequently isolated group of bacteria in milk samples of dairy cows and are associated with (mild) clinical and subclinical mastitis. Different species and strains of NAS differ in their epidemiology, pathogenicity, virulence, ecology and host adaptation, and antimicrobial resistance profiles. They have distinct relationships with the microbiome composition of the udder and may also have protective effects against other mastitis pathogens. Some appear to persist on the skin and in the teat canal and udder, while others seem to be transient residents of the udder from the environment. Analyzing genotypic and phenotypic differences in individual species may also hold clues to why some appear more successful than others in colonizing the udder. Understanding species-level interactions within the microbiome and its interactions with host genetics will clarify the role of NAS in bovine mastitis and udder health.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.156
GPT teacher head0.367
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations144
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Veterinary ScienceSame topicMilk Quality and Mastitis in Dairy CowsFrench-language works237,207