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Record W3105276055 · doi:10.1111/jfd.13300

Impact of feed restriction, chloroquine and deoxynivalenol on viral haemorrhagic septicaemia virus IVb in fathead minnow <i>Pimephales promelas</i> Rafinesque

2020· article· en· W3105276055 on OpenAlexafffund
Juan‐Ting Liu, John S. Lumsden

Bibliographic record

VenueJournal of Fish Diseases · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Guelph
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPimephales promelasMinnowBiologyLarvicideChloroquineVirusLarvaVirologyVeterinary medicineFish <Actinopterygii>Animal scienceAedes aegyptiFisheryImmunologyEcologyMalaria

Abstract

fetched live from OpenAlex

Abstract Autophagy can markedly alter host response to infectious disease, and several studies have demonstrated that a restricted diet or deoxynivalenol modulates autophagy and reduces mortality of fish due to bacterial disease. The picture is less clear for viral diseases of fish. Duplicate tanks of fathead minnow, Pimephales promelas Rafinesque, were fed a replete diet (control), 100 µM chloroquine, 5 µM deoxynivalenol, 10% (fasted) or 40% of a replete diet (pair‐fed) for 2 weeks and then experimentally infected by intraperitoneal injection with 2 × 10 5 viral haemorrhagic septicaemia virus IVb. Survival from highest to lowest for the different treatments was as follows: deoxynivalenol (average 43.3%); control (40.0%); pair‐fed (35.0%); fasted (33.3%); and chloroquine (21.7%). No treatment significantly altered the survival rate of fathead minnow after VHSV IVb infection when compared to controls; however, the fish fed with chloroquine had significantly lower survival rate than the fish fed deoxynivalenol ( p &lt; .05).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.710

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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