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Record W2119030995 · doi:10.2217/fmb.09.132

Metabolomics: Towards Understanding Host–Microbe Interactions

2010· review· en· W2119030995 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFuture Microbiology · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsMetabolomicsComputational biologyBiologyHost (biology)CheminformaticsMetabolomeBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Metabolomics employs an array of analytical techniques, including high-resolution nuclear magnetic resonance spectroscopy and mass spectrometry, to simultaneously analyze hundreds to thousands of small-molecule metabolites in biological samples. In conjunction with chemoinformatics and bioinformatics tools, metabolomics enables comprehensive characterization of the metabolic phenotypes (metabotypes) of the human, and other mammalian, hosts that have co-evolved with a large number of diverse commensal microbes, especially in the intestinal tract. Correlation of the metabotypes with the microbial profiles derived from culture-independent molecular techniques is increasingly helping to decipher inherent and intimate host-microbe relationships. This integrated, systems biology approach is improving our understanding of the molecular mechanisms underlying multilevel host-microbe interactions, and promises to elucidate the etiologies of human disorders resulting from unfavorable human-microbial associations, including enteric infections.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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