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Record W4401302484 · doi:10.1111/jeu.13045

<i>Nosema bombycis</i>: A remarkable unicellular parasite infecting insects

2024· review· en· W4401302484 on OpenAlexaff
Qingyuan Huang, Wanying Hu, Xianzhi Meng, Jie Chen, Guoqing Pan

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of Toronto
FundersCentre Scientifique et Technique du BâtimentFundamental Research Funds for the Central UniversitiesNatural Science Foundation of ChongqingChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsMicrosporidiaBiologyMicrosporidiosisNosemaParasite hostingZoologyProtozoaVirologyMicrobiologySpore

Abstract

fetched live from OpenAlex

Microsporidia are opportunistic fungal-like pathogens that cause microsporidiosis, which results in significant economic losses and threatens public health. Infection of domesticated silkworms by the microsporidium Nosema bombycis causes pébrine disease, for which this species of microsporidia has received much attention. Research has been conducted extensively on this microsporidium over the past few decades to better understand its infection, transmission, host-parasite interaction, and detection. Several tools exist to study this species including the complete genome sequence of N. bombycis. In addition to the understanding of N. bombycis being important for the silkworm industry, this species has become a model organism for studying microsporidia. Research on biology of N. bombycis will contribute to the development of knowledge regarding microsporidia and potential antimicrosporidia drugs. Furthermore, this will provide insight into the molecular evolution and functioning of other fungal pathogens.

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 score0.004
Threshold uncertainty score0.012

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.032
GPT teacher head0.322
Teacher spread0.290 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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