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Record W1992762269 · doi:10.1099/mic.0.26265-0

Arming the enemy: the evolution of resistance to self-proteins

2003· review· en· W1992762269 on OpenAlexaff
Graham Bell, Pierre‐Henri Gouyon

Bibliographic record

VenueMicrobiology · 2003
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdversaryCompromiseBiologyInnate immune systemBacteriaAntibiotic resistanceResistance (ecology)MicrobiologyAntibioticsImmune systemImmunologyComputer securityEcologyGeneticsPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

A remarkable range of novel antibiotics is attracting increasing interest as a major new weapon in the campaign against bacterial infection. They are based on the toxic peptides that provide the innate immune system of animals, and it is claimed that bacteria will be unable to evolve resistance to them because they attack the 'Achilles' heel' of bacterial membrane structure. Both experimental evidence and theoretical arguments suggest that this claim is doubtful. If so, the introduction of these substances into general use may provoke the evolution of resistance to our own defence proteins and thus compromise our natural defences against infection.

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)0.000
metaresearch head score (Gemma)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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

Citations137
Published2003
Admission routes1
Has abstractyes

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