MétaCan
Menu
Back to cohort
Record W3092798646 · doi:10.1111/jfd.13261

Development of a <i>Piscirickettsia salmonis</i> immersion challenge model to investigate the comparative susceptibility of three salmon species

2020· article· en· W3092798646 on OpenAlexaff
Amy Long, Aidan Goodall, Simon R. M. Jones

Bibliographic record

VenueJournal of Fish Diseases · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSalmoBiologyOncorhynchusZoologyLepeophtheirusFisheryPathogenJuvenileAquatic animalSalmonidaeEcologyFish <Actinopterygii>Microbiology

Abstract

fetched live from OpenAlex

Abstract Piscirickettsia salmonis , the aetiological agent of salmonid rickettsial septicaemia (SRS), is a global pathogen of wild and cultured marine salmonids. Here, we describe the development and application of a reproducible, standardized immersion challenge model to induce clinical SRS in juvenile pink ( Oncorhynchus gorbuscha ), Atlantic ( Salmo salar ) and sockeye salmon ( O. nerka ). Following a 1‐hr immersion in 10 5 colony‐forming units/ml, cumulative mortality in Atlantic salmon was 63.2% while mortality in sockeye salmon was 10%. Prevalence and levels of the bacterium in kidney prior to onset of mortality were lower in sockeye compared with Atlantic or pink salmon. The timing and magnitude of bacterial shedding were estimated from water samples collected during the exposure trials. Shedding was estimated to be 82‐fold higher in Atlantic salmon as compared to sockeye salmon and peaked in the Atlantic salmon trial at 36 d post‐immersion. These data suggest sockeye salmon are less susceptible to P. salmonis than Atlantic or pink salmon. Finally, skin lesions were observed on infected fish during all trials, often in the absence of detectable infection in kidney. As a result, we hypothesize that skin is the primary point of entry for P. salmonis during the immersion challenge.

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)0.000
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 score0.351
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.076
GPT teacher head0.269
Teacher spread0.193 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fish DiseasesSame topicAquaculture disease management and microbiotaFrench-language works237,207