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Record W2981597633 · doi:10.1111/jfd.13090

<i>Saprolegnia</i> molecular phylogeny among farmed teleosts in Nova Scotia, Canada

2019· article· en· W2981597633 on OpenAlexafffundabout
Mohammad Nasif Sarowar, R. Cusack, J. Duston

Bibliographic record

VenueJournal of Fish Diseases · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsNova Scotia Department of AgricultureDalhousie University
FundersNova Scotia Department of Fisheries and Aquaculture
KeywordsSaprolegniaBiologySalmoBrown troutSalvelinusPhylogenetic treeZoologyOomyceteTroutNova scotiaBotanyFisheryMicrobiologyFish <Actinopterygii>GeneticsGene

Abstract

fetched live from OpenAlex

To identify the pathogens causing saprolegniosis among farmed fish in Nova Scotia, 172 infected tissues and 23 water samples were collected from six species of teleosts: Atlantic salmon (Salmo salar), brown trout (Salmo trutta), Arctic charr (Salvelinus alpinus), brook trout (Salvelinus fontinalis), striped bass (Morone saxatilis) and rainbow trout (Oncorhynchus mykiss) at nine facilities over a 600 km range. Following laboratory culture, 132 isolates were recovered. Six species of oomycetes were identified from analysis of the internal transcribed spacer (ITS) sequence of the nrDNA: Saprolegnia parasitica, Saprolegnia ferax, Saprolegnia diclina, Saprolegnia aenigmatica, Saprolegnia torulosa, Saprolegnia sp. and Pythiopsis cymosa. Further phylogenetic analyses of the ITS and cytochrome c oxidase subunit 1 (Cox1) regions revealed four strains of Saprolegnia parasitica (named here as S1, S2, S3 and S4), of which S1 and S2 were common (37% and 42% of the isolates), and two strains of S. ferax. Among S. parasitica, S2 and S3 are more closely related to each other than to S1 based on the phylogenetic analyses and predicted RNA secondary structure of the ITS region. Sexual structures with a similar morphology were formed by S1 and S3 in vitro, but were not formed by S2.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.976

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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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 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

Citations24
Published2019
Admission routes3
Has abstractyes

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