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Record W2620962510 · doi:10.3389/978-2-88945-181-4

Antimicrobial Resistance and Virulence Common Mechanisms

2017· book· en· W2620962510 on OpenAlexaff
Étienne Giraud, Ivan Rychlı́k, Axel Cloeckaert

Bibliographic record

VenueFrontiers research topics · 2017
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesParacelsus Medizinische PrivatuniversitätNational Institutes of HealthMinisterio de Economía y CompetitividadKey Technologies Research and Development ProgramNational Natural Science Foundation of ChinaDanmarks Tekniske UniversitetInstituto de Salud Carlos IIIJoint Programming Initiative Water challenges for a changing worldComunidad de Madrid
KeywordsVirulenceAntimicrobialResistance (ecology)Antibiotic resistanceMicrobiologyBiologyGeneticsAntibioticsEcology

Abstract

fetched live from OpenAlex

Since the past few years, there is increasing evidence that multi-drug resistant pathogens may become more virulent than their antibiotic-suceptible counterparts. This Research Topic will focus on common mechanisms that affect both virulence and antimicrobial resistance. It may include studies where resistance and virulence share common effectors such as efflux pumps, or studies about global regulatory systems acting on both resistance and virulence (co-regulation). Also of particular interest will be studies about mobile genetic elements such as plasmids or genomic islands that carry both antimicrobial resistance and virulence genes, therefore favoring their co-selection. Studies reporting the co-evolution of antimicrobial resistance and virulence in particular pathogenic clones are also welcome in this Topic.

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: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.005

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 head0.027
GPT teacher head0.316
Teacher spread0.289 · 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
GenreOther

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

Citations6
Published2017
Admission routes1
Has abstractyes

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