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Record W4396957825 · doi:10.1139/facets-2023-0156

Modelling the dispersion of infectious salmon anemia virus from Atlantic salmon farms in the Quoddy Region of New Brunswick, Canada and Maine, USA

2024· article· en· W4396957825 on OpenAlexafffundvenueabout
Fuhong Ding, Nellie Gagné, Delphine Ditlecadet, Brady K. Quinn, Marc Trudel

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsFisheryGeographyEquine infectious anemiaBiologyVirusVirology

Abstract

fetched live from OpenAlex

Pathogen dispersal from infected aquaculture sites into the surrounding ocean poses risks of infection to wild and farmed species, but is difficult to predict. This study aimed to build a framework using an ocean circulation and a particle tracking model in conjunction with a dynamic infection model and a virus inactivation model to simulate the dispersal of the infectious salmon anemia virus (ISAV) from Atlantic salmon farms. Simulated particles were released from hypothetically infected farms and advected by modelled currents. Inactivation of viral cohorts by ambient ultraviolet radiation and natural microbial communities was simulated during advection. Simulations showed that ISAV concentration varied spatiotemporally with the progression of the outbreak, current speed and direction, tidal elevation amplitude, and environmental decay. Connectivity among aquaculture sites varied in relation to seaway distances, though simulations showed that connectivity can also be asymmetrical between farm sites. Sensitivity analyses showed that the dispersal of ISAV was moderately sensitive to uncertainty associated with the viral decay model, highlighting the importance of obtaining accurate estimates of inactivation rates of ISAV. This framework provides an approach to simulate waterborne viral transmission that considers the biology and epidemic features of significance for pathogens and the dynamic conditions of the ocean.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.344

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations1
Published2024
Admission routes4
Has abstractyes

Explore more

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