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Record W4282912978 · doi:10.1016/j.isci.2022.104588

A parasite outbreak in notothenioid fish in an Antarctic fjord

2022· article· en· W4282912978 on OpenAlexaff
Thomas Desvignes, Henrik Lauridsen, A. Valdivieso, Rafaela S. Fontenele, Simona Kraberger, Katrina N. Murray, Nathalie R. Le François, H. William Detrich, Michael L. Kent, Arvind Varsani, John H. Postlethwait

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMontreal BiodomeEspace pour la vie
FundersOffice of Polar ProgramsUniversity of OregonRussell Sage FoundationNational Institutes of HealthNational Science Foundation
KeywordsBiologyOutbreakBiodiversityZoologyEcologyFjordPlanktonParasite hostingOceanography

Abstract

fetched live from OpenAlex

Climate changes can promote disease outbreaks, but their nature and potential impacts in remote areas have received little attention. In a hot spot of biodiversity on the West Antarctic Peninsula, which faces among the fastest changing climates on Earth, we captured specimens of two notothenioid fish species affected by large skin tumors at an incidence never before observed in the Southern Ocean. Molecular and histopathological analyses revealed that X-cell parasitic alveolates, members of a genus we call Notoxcellia , are the etiological agent of these tumors. Parasite-specific molecular probes showed that xenomas remained within the skin but largely outgrew host cells in the dermis. We further observed that tumors induced neovascularization in underlying tissue and detrimentally affected host growth and condition. Although many knowledge gaps persist about X-cell disease, including its mode of transmission and life cycle, these findings reveal potentially active biotic threats to vulnerable Antarctic ecosystems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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