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Record W4220663454 · doi:10.1128/mbio.02949-21

The CIAMIB: a Large and Metabolically Diverse Collection of Inflammation-Associated Bacteria from the Murine Gut

2022· article· en· W4220663454 on OpenAlexafffund
Erin Oi-Yan Wong, Emma Brownlie, Katharine M. Ng, Sornnujah Kathirgamanathan, Feiqiao Brian Yu, Bryan D. Merrill, Kerwyn Casey Huang, Alberto Martín, Carolina Tropini, William Wiley Navarre

Bibliographic record

VenuemBio · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British ColumbiaUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchGovernment of CanadaNational Institutes of HealthW. Garfield Weston FoundationNational Institute of General Medical Sciences
KeywordsLachnospiraceaeInflammationBiologyGut floraBacteriaMicrobiologyGut bacteriaMicrobiomeImmunologyBioinformaticsGeneticsFirmicutes

Abstract

fetched live from OpenAlex

Attempts to explore the role of the microbiota in animal physiology have resulted in large-scale efforts to cultivate the thousands of microbes that are associated with humans. In contrast, relatively few lab mouse-associated bacteria have been isolated, despite the fact that the overwhelming number of studies on the microbiota use laboratory mice that are colonized with microbes that are quite distinct from those in humans. Here, we report the results of a large-scale isolation of bacteria from the intestines of laboratory mice either prone to or suffering from gut inflammation. This collection comprises dozens of novel isolates, many of which represent the only cultured representatives of their genus or species. We report their basic growth characteristics and genomes and are making them widely available to the greater research community.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.221
Teacher spread0.216 · 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 designBench or experimental
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

Citations22
Published2022
Admission routes2
Has abstractyes

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