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Record W2158797036 · doi:10.1098/rstb.2007.2044

Antibiotics as signalling molecules

2007· review· en· W2158797036 on OpenAlexaff
Grace Yim, Helena Huimi Wang, Julian Davies FRS

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2007
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyGeneSignallingAntibioticsCell signalingSignalling pathwaysTranscription (linguistics)Transcription factorGene expressionSignal transductionMicrobiologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

We present the argument that the majority of low-molecular-weight organic compounds made and secreted by microbes play roles as cell-signalling molecules in the environment. Of the large number of compounds isolated to date, only a small fraction have been shown to possess useful therapeutic antibiotic activity. However, most microbial metabolites modulate gene transcription at low concentrations, and this is proposed to be the primary effect of the compounds in the maintenance of microbial communities in the environment. Thus, microbial metabolites constitute a large collection of cell-signalling molecules that regulate gene expression in microbial populations and possibly the interactions of these populations with the surrounding organisms.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.131
GPT teacher head0.323
Teacher spread0.193 · 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
GenreReview

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

Citations492
Published2007
Admission routes1
Has abstractyes

Explore more

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