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Record W4281774004 · doi:10.1016/j.pt.2022.05.007

Microsporidia: a new taxonomic, evolutionary, and ecological synthesis

2022· review· en· W4281774004 on OpenAlexafffund
Jamie Bojko, Grant D. Stentiford, Ben Williams, Martin S. J. Rogers, David Bass

Bibliographic record

VenueTrends in Parasitology · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of Toronto
FundersNatural Environment Research CouncilCanadian Institutes of Health ResearchSight Research UKDepartment for Environment, Food and Rural Affairs, UK GovernmentAlfred P. Sloan Foundation
KeywordsMicrosporidiaCladeBiologyTaxonomic rankEvolutionary biologyPhylogeneticsLineage (genetic)EcologyZoologyTaxonGeneGenetics

Abstract

fetched live from OpenAlex

Microsporidian diversity is vast. There is a renewed drive to understand how microsporidian pathological, genomic, and ecological traits relate to their phylogeny. We comprehensively sample and phylogenetically analyse 125 microsporidian genera for which sequence data are available. Comparing these results with existing phylogenomic analyses, we suggest an updated taxonomic framework to replace the inconsistent clade numbering system, using informal taxonomic names: Glugeida (previously clades 5/3), Nosematida (4a), Enterocytozoonida (4b), Amblyosporida (3/5), Neopereziida (1), and Ovavesiculida (2). Cellular, parasitological, and ecological traits for 281 well-defined species are compared with identify clade-specific patterns across long-branch Microsporidia. We suggest that future taxonomic circumscriptions of Microsporidia should involve additional markers (SSU/ITS/LSU), and that a comprehensive suite of phenotypic and ecological traits help to predict broad microsporidian functional and lineage diversity.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.060
GPT teacher head0.357
Teacher spread0.297 · 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

Citations156
Published2022
Admission routes2
Has abstractyes

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