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Record W2797018969 · doi:10.3354/dao03234

Histopathological, ultrastructural and molecular phylogenetic analysis of a novel microsporidium in a loggerhead sea turtle Caretta caretta

2018· article· en· W2797018969 on OpenAlexaffabout
SA Martinson, Spencer J. Greenwood, D Wadowska, Karin Martin

Bibliographic record

VenueDiseases of Aquatic Organisms · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBiologySea turtlePolar filamentUltrastructureTurtle (robot)SporeZoologyPhylogenetic treeAnatomyMicrosporidiaFisheryMicrobiologyGeneGenetics

Abstract

fetched live from OpenAlex

Microsporidial spores were identified in the musculature of a loggerhead sea turtle Caretta caretta found dead on the shore in New Brunswick, Canada. Gastroenteritis was diagnosed on gross postmortem examination, with no gross abnormalities detected in the skeletal muscle. Histologically, the microsporidial spores were associated with inflammation and muscular necrosis and measured 1.1-1.7 × 2.2-3.4 µm. Spores were typically identified within sporophorous vesicles and, less often, in sporophorocysts and were weakly Gram positive, had punctate PAS staining, and were occasionally strongly acid-fast. Ultrastructural characteristics included 7-10 polar filament coils and other standard features of microsporidial spores. PCR for the microsporidial small subunit rRNA gene sequence was performed on DNA extracted from the muscle and small intestine, and the resulting amplicon was sequenced and queried against published microsporidial genomes. DNA sequences shared 98.2-99.8% sequence identity to Clade III of the Marinosporidia. This is the first report of a microsporidial infection contributing to the mortality of a sea turtle.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 studies0.0000.000
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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