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Record W2030537372 · doi:10.3354/dao067081

Subclinical Listonella anguillarum infection does not impair recovery of swimming performance in rainbow trout Oncorhynchus mykiss

2005· article· en· W2030537372 on OpenAlexaff
Keith B. Tierney, SK Balfry, A. P. Farrell

Bibliographic record

VenueDiseases of Aquatic Organisms · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsRainbow troutVibrio anguillarumBiologySubclinical infectionFisheryPathogenTroutSalmoFish <Actinopterygii>MicrobiologyVibrioVirologyBacteria

Abstract

fetched live from OpenAlex

This study examines whether injections of the commonly used bacterial-challenge pathogen Listonella anguillarum (formerly Vibrio anguillarum) negatively impact the ability of rainbow trout Oncorhynchus mykiss Walbaum to perform repeat swimming trials. Fish were given intraperitoneal injections of either a sub-lethal (10(5) colony forming units; CFUs) or a lethal (10(7) CFUs) dose of L. anguillarum, held for 48 h, and then given 2 successive ramp critical swimming speed (Ucrit) tests separated by 45 min. Compared with saline-injected control fish, the low-dose injection did not significantly impair swimming performance and recovery. Similarly, Ucrit and re-performance for fish surviving the high-dose injection were comparable to control (2 of 6 fish died after injection and before testing). In contrast, a positive control test of seawater challenge did impair recovery of swimming performance. In view of these results and common use of L. anguillarum as a challenge pathogen for toxicological studies, it seems unlikely that the consequences of pathogenesis impact the important cardiorespiratory changes associated with exercise.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.237
Teacher spread0.230 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

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