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Microheterogeneity and Coevolution: An Examination of rDNA Sequence Characteristics in <i>Neoparamoeba pemaquidensis</i> and Its Prokinetoplastid Endosymbiont

2007· article· en· W2169772031 on OpenAlexafffund
Charles Caraguel, Charles J. O’Kelly, Pierre Legendre, Salvatore Frasca, Rebecca J. Gast, B Despres, Richard J. Cawthorn, Spencer J. Greenwood

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversity of Prince Edward Island
FundersAtlantic Canada Opportunities AgencyAustralian Government
KeywordsBiologyCoevolutionEvolutionary biologySequence (biology)Genetics

Abstract

fetched live from OpenAlex

Neoparamoeba pemaquidensis, the etiological agent of amoebic gill disease, has shown surprising sequence variability among different copies of the 18S ribosomal RNA gene within an isolate. This intra-genomic microheterogeneity was confirmed and extended to an analysis of the internal transcribed spacer (ITS) region. High levels of intra-genomic nucleotide diversity (Pi=0.0201-0.0313) were found among sequenced ITS regions from individual host amoeba isolates. In contrast, the ITS region of its endosymbiont revealed significantly lower levels of intra-genomic nucleotide diversity (Pi=0.0028-0.0056) compared with the host N. pemaquidensis. Phylogenetic and ParaFit coevolution analyses involving N. pemaquidensis isolates and their respective endosymbionts confirmed a significant coevolutionary relationship between the two protists. The observation of non-shared microheterogeneity and coevolution emphasizes the complexity of the interactions between N. pemaquidensis and its obligate endosymbiont.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 designObservational
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

Citations32
Published2007
Admission routes2
Has abstractyes

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