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Record W4414776112 · doi:10.1177/10406387251377526

Insights from 17 years of culture and PCR detection of animal mollicutes in a Canadian provincial laboratory

2025· article· en· W4414776112 on OpenAlexaffabout
Lisa Ledger, Fernando Munevar, Pauline Nelson-Smikle, Calvin Kellendonk, Qiumei You, Lois Parker, Patricia McRaild, Rebeccah McDowall, Jason Eidt, Nathan Benoit, Pat Bell-Rogers, Grant Maxie, Hugh Y. Cai

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMollicutesMycoplasmaPolymerase chain reactionAnimal healthGold standard (test)DNA sequencingUreaplasmaAnimal speciesIdentification (biology)

Abstract

fetched live from OpenAlex

Since ~1980, the Animal Health Laboratory (AHL) in Ontario, Canada, has isolated animal mollicute species by culture. Data for the most recent 17 y (2007–2024) captures over 90,000 test results. Advancements in PCR, qPCR, and DNA sequencing have shifted the percentage of testing by PCR from 18.7% in 2007 to 91.1% in 2024. The bulk of this shift is due to the uptake of molecular testing as a screening tool for clinically normal animals, but this shift has not been universal, particularly for ureaplasma testing. Culture remains the gold standard for the detection and identification of rare pathogens and plays a key role in research through our mycoplasma cryobank, which includes 40+ y of isolates. Synergizing the microbiologic and molecular techniques developed over the AHL’s multi-decade history has presented novel opportunities for detection, characterization, and local eradication of animal mollicutes, including the development of new assays, tracking of historical trends for antimicrobial resistance (AMR), and identifying AMR-associated mutations in Mycoplasmopsis ( Mycoplasma ) bovis .

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)0.016
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.276
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0040.008
Scholarly communication0.0070.003
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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
Published2025
Admission routes2
Has abstractyes

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