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Record W2920323856 · doi:10.1128/msystems.00098-18

Comprehensive Virulence Gene Profiling of Bovine Non- <i>aureus</i> Staphylococci Based on Whole-Genome Sequencing Data

2019· article· en· W2920323856 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuemSystems · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
FundersAlberta Livestock and Meat AgencyUniversity of Calgary
KeywordsBiologyMastitisGeneVirulenceImmune systemGenomeMicrobiologyGeneticsComputational biology

Abstract

fetched live from OpenAlex

staphylococci (NAS) are the most frequently isolated pathogens from milk in dairy cattle worldwide. The virulence factors (VFs) and mechanisms by which these bacteria cause udder infection are not fully known. We determined the distribution and associations of 191 VFs in 25 NAS species and investigated the relationship between VFs and disease. Although the overall number of VFs was not associated with disease severity, increasing numbers of toxin and host immune evasion genes specifically were associated with more severe disease outcomes. These findings suggest that the development of disease and the interactions of VFs with the host are complex and determined by the interplay of genes rather than just the presence of virulence genes. Together, our results provide foundational genetic knowledge to other researchers to design and conduct further experiments, focusing on understanding the synergy between VFs and roles of individual NAS species in IMI and characterizing species-specific effects on 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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.334

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.251
Teacher spread0.194 · 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