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Record W4415436813 · doi:10.1099/acmi.0.001015.v3

Notification of bacterial strains made available by the UK National Collection of Type Cultures in 2023

2025· article· en· W4415436813 on OpenAlexaff
Jake D. Turnbull, Jo Dicks, Rachael Adkin, Alexander Dickinson, Dorota Kaushal, Mojisola Semowo

Bibliographic record

VenueAccess Microbiology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsBell (Canada)
FundersUniversity of Central Florida
KeywordsMetadataPublic healthFunction (biology)Accession number (library science)Resource (disambiguation)Public access

Abstract

fetched live from OpenAlex

Many microbial culture collections, like the UK's National Collection of Type Cultures, add biological material such as bacterial strains to their holdings over time in a process known as accessioning. Here, we report on the 101 bacterial strains made available to scientists in the UK and globally by the National Collection of Type Cultures in 2023. Strains that are received are preserved, identified to species level and confirmed to be viable and pure. Genomic and metadata (where these are available) are made accessible via the UKHSA Culture Collections online catalogue. Commentary on the strains' provenance and significance is presented, and wider trends in accessioning between 2017 and 2023 are examined. On average, ~101 strains were made available to the scientific community each year between 2017 and 2023. Fewer strains of veterinary provenance were made available than any other kind of strain, highlighting a need to accession more of these strains. However, there has been growth in the proportion of strains that are either antimicrobial resistant or type strains of novel microbial taxa, demonstrating that the NCTC program of accessioning helps support its function as a contemporary public health resource and repository for prokaryotic taxonomists.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.229

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 science0.0000.000
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 head0.022
GPT teacher head0.308
Teacher spread0.285 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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