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Record W4396609785 · doi:10.1016/j.vetmic.2024.110104

Staphylococcal carriage among captive dingoes (Canis dingo) in Victoria, Australia

2024· article· en· W4396609785 on OpenAlexaff
Sarah M. Wigmore, Scott Nankervis, Lynette Watson, David C. Bean

Bibliographic record

VenueVeterinary Microbiology · 2024
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsDingoBiologyStaphylococcus pseudintermediusZoologyCanisCarriageAntibiotic resistanceMicrobiologyStaphylococcusEcologyAntibioticsStaphylococcus aureusGeneticsBacteria

Abstract

fetched live from OpenAlex

The evolutionary lineage and taxonomy of the Australian dingo is controversial, however recent genomic and gut metagenomic research has suggested that dingoes are evolutionarily distinct from modern dogs. Staphylococcus species are known commensal organisms of dogs and other mammals. In this study we took the opportunity to determine the carriage rate and antimicrobial resistance profiles of Staphylococcus species from 15 captive Australian dingoes. S. pseudintermedius was the only coagulase-positive species recovered, isolated from 6/15 (40%) and 9/13 (69%) of the animals during the 2020 (winter) and 2021 (summer) sampling times, respectively. Twenty-three coagulase-negative isolates were characterised, with S. equorum being the most frequently (20/23, 87%) recovered species. Two isolates of S. equorum had their genomes sequenced to learn more about this species. Antimicrobial resistance amongst both coagulase-positive and -negative isolates was low; with resistance to only 3 of 12 antimicrobials observed: penicillin, erythromycin, and trimethoprim. We have shown that the Australian dingo is a host organism for S. pseudintermedius much like it is in dogs, however the carriage rate was lower than has previously been reported from dogs in Australia.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.141
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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
Published2024
Admission routes1
Has abstractyes

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