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Record W4214733202 · doi:10.1111/raq.12665

<i>Edwardsiella ictaluri</i>: A systemic review and future perspectives on disease management

2022· review· en· W4214733202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReviews in Aquaculture · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMemorial University of Newfoundland
FundersKing Mongkut's University of Technology Thonburi
KeywordsEdwardsiella ictaluriIctalurusCatfishAquacultureOutbreakBiosecurityBiologyPangasiusBacterial diseaseVeterinary medicineDiseaseFish farmingMicrobiologyBiotechnologyFisheryFish <Actinopterygii>EcologyVirologyMedicine

Abstract

fetched live from OpenAlex

Abstract Edwardsiella ictaluri , a non‐zoonotic Gram‐negative bacterium, has been known to science for more than 4 decades. It was reported for the first time in 1979 in Ictalurus punctatus in the USA and later in Pangasianodon hypophthalmus and Pelteobagrus fulvidraco in Asia. Even though catfish species are more susceptible to E. ictaluri , other fish species are also affected, and up to 44 fish species in four continents are known to be susceptible. The diseases caused by E. ictaluri are known as enteric septicaemia of catfish (ESC) in channel catfish, bacillary necrosis of pangasius (BNP) in striped catfish, red head disease in yellow catfish and edwardsiellosis in tilapia. Outbreaks caused by E. ictaluri can cause up to 100% mortality resulting in substantive economic losses to the industry, threatening food security and undermining sustainability. Although efforts have been made to prevent and control this pathogen using vaccines, antibiotics, disease resistance selective breeding, functional feed ingredients, prebiotics and probiotics, and biosecurity measures, E. ictaluri is still causing health issues in different countries. Here, we provided with a comprehensive review that addressed the current knowledge of E. ictaluri bacteriological characteristics, epidemiology, pathogenesis, diagnosis, control and management. Furthermore, we also provided the future perspectives based on advanced technologies and biosecurity management in aquaculture to assist pathogen control and/or eradication.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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