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Record W7077146118 · doi:10.5281/zenodo.15676385

Antibiotic resistant bacteria in ready-to-eat meat products from Switzerland

2025· dataset· en· W7077146118 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsSociété Québécoise de Néphrologie
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsStaphylococcus aureusContext (archaeology)Mobile genetic elementsColistinBacteriaMultiple drug resistance

Abstract

fetched live from OpenAlex

This dataset supports a study investigating the prevalence and characteristics of antibiotic-resistant bacteria in ready-to-eat meat products from Switzerland. With antimicrobial resistance remaining a major global health concern—particularly in the context of intensive livestock production—this study examines ready-to-eat meat products as potential reservoirs for antibiotic resistant bacteria. A total of 804 ready-to-eat meat products were collected from butcheries across Switzerland. Selective culture methods were used to isolate presumptive antibiotic-resistant bacteria, specifically targeting vancomycin-resistant Enterococci (VRE), extended-spectrum beta-lactamase-producing Enterobacterales (ESBL), carbapenem-resistant Enterobacterales (CRE), and methicillin-resistant Staphylococcus aureus (MRSA). The "phenotypic resistance dataset" contains antibiotic susceptibility testing results for the isolates, performed using broth microdilution. It reports minimum inhibitory concentration (MIC) values for up to 31 antibiotics (Enterobacterales) or 24 antibiotics (Enterococci). Colistin resistance was assessed separately using the drop test. The dataset also includes multidrug resistance classifications (e.g., MDR index). The "resistance gene and mobile genetic element annotations" include resistance genes identified via AMRFinder Plus, classified by antibiotic class (e.g., beta-lactams, cephalosporins, carbapenems, metals). Mobile genetic elements were identified using MobileElementFinder, and plasmid typing was performed using MOB-suite. The "ARG localization dataset" categorizes resistance genes based on their genomic location—plasmid, chromosome, or phage—using SourceFinder. Key findings from the dataset include the recovery of 177 antibiotic-resistant bacterial isolates, of which 148 were multidrug-resistant (resistant to ≥3 antibiotic classes). These included third-generation cephalosporin-resistant Enterobacterales, vancomycin-resistant Enterococci, and one methicillin-resistant Staphylococcus aureus (MRSA). All isolates remained susceptible to critical last-resort antibiotics such as carbapenems and colistin. Whole genome sequencing of 31 representative isolates revealed 164 unique resistance genes across 25 resistance classes, including those conferring resistance to beta-lactams, cephalosporins, tetracyclines, and macrolides. Co-selection factors such as metal resistance genes were also identified. Most ARGs were chromosomally encoded, though several were plasmid-borne, indicating potential for horizontal gene transfer. The dataset includes isolate metadata, phenotypic resistance profiles, and annotated resistance gene information. Sequencing data (both short- and long-read) are available via the National Center for Biotechnology Information (NCBI).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.025
GPT teacher head0.233
Teacher spread0.208 · 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
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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