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Record W1987218056 · doi:10.1002/bies.201000091

The proportional lack of archaeal pathogens: Do viruses/phages hold the key?

2011· article· en· W1987218056 on OpenAlexafffund
Erin E. Gill, Fiona S. L. Brinkman

Bibliographic record

VenueBioEssays · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchSimon Fraser UniversityMichael Smith Health Research BC
KeywordsArchaeaBiologyBacteriaVirulenceHorizontal gene transferBacterial virusMicrobiologyVirusBacteriophageVirologyGeneticsGenomeGeneEscherichia coli

Abstract

fetched live from OpenAlex

Although Archaea inhabit the human body and possess some characteristics of pathogens, there is a notable lack of pathogenic archaeal species identified to date. We hypothesize that the scarcity of disease-causing Archaea is due, in part, to mutually-exclusive phage and virus populations infecting Bacteria and Archaea, coupled with an association of bacterial virulence factors with phages or mobile elements. The ability of bacterial phages to infect Bacteria and then use them as a vehicle to infect eukaryotes may be difficult for archaeal viruses to evolve independently. Differences in extracellular structures between Bacteria and Archaea would make adsorption of bacterial phage particles onto Archaea (i.e. horizontal transfer of virulence) exceedingly hard. If phage and virus populations are indeed exclusive to their respective host Domains, this has important implications for both the evolution of pathogens and approaches to infectious disease control.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.076
GPT teacher head0.279
Teacher spread0.204 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations46
Published2011
Admission routes2
Has abstractyes

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