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Record W1987620662 · doi:10.3354/dao062093

SSU rRNA gene sequence reveals two genotypes of Spironucleus barkhanus (Diplomonadida) from farmed and wild Arctic charr Salvelinus alpinus

2004· article· en· W1987620662 on OpenAlexaboutno aff
Anders Palmstrøm Jørgensen, E Sterud

Bibliographic record

VenueDiseases of Aquatic Organisms · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsSalvelinusBiologyArcticHatcheryZoologyFish farmingFisheryAquacultureEcologyTroutFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Spironucleus barkhanus isolated from the blood of Arctic charr Salvelinus alpinus from a marine fish farm were genetically compared with S. barkhanus isolated from the gall bladder of wild Arctic charr. The wild Arctic charr were caught in the lake used as the water source for the hatchery from which the farmed fish originated. Sequencing of the small subunit ribosomal RNA gene (SSU rDNA) from these 2 populations showed that the isolates obtained from farmed and wild Arctic charr were only 92.7 % similar. Based on the sequence differences between these isolates, it is concluded that the parasites isolated from the farmed fish have not been transmitted from wild Arctic charr in the hatchery's fresh water source. It is therefore most likely that the farmed fish were infected by S. barkhanus after they were transferred to seawater. S. barkhanus isolated from diseased farmed Arctic charr were 99.7% similar to the isolates obtained from diseased farmed Chinook (Canada) and Atlantic salmon (Norway). The high degree of sequence similarity between S. barkhanus from farmed Arctic charr, Chinook and Atlantic salmon indicates that systemic spironucleosis may be caused by specific strains/variants of this parasite. The genetic differences between the isolates of farmed and wild fish are of such magnitude that their conspecificity should be questioned.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2004
Admission routes1
Has abstractyes

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