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Record W2116327784 · doi:10.3382/ps.2007-00195

Development of Methods for Detection and Quantification of Avian Influenza and Newcastle Disease Viruses in Compost by Real-Time Reverse Transcription Polymerase Chain Reaction and Virus Isolation

2008· article· en· W2116327784 on OpenAlexafffund
Jiewen Guan, M. Chan, Bo Ma, C. Grenier, D.C. Wilkie, John Pasick, Brian W. Brooks, J. L. Spencer

Bibliographic record

VenuePoultry Science · 2008
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsAgriculture and Agri-Food CanadaCanadian Food Inspection Agency
FundersUniversity of Ottawa
KeywordsNewcastle diseaseEmbryonatedCompostVirusBiologyVirologyReverse transcription polymerase chain reactionReal-time polymerase chain reactionMicrobiologyAgronomyGeneGene expression

Abstract

fetched live from OpenAlex

Composting has been used for disposal of poultry carcasses and manure following outbreaks caused by avian influenza virus (AIV) and Newcastle disease virus (NDV), but methods are needed to test for survival of the viruses in compost to ensure biosecurity. Methods developed in the present study include extracting viruses from compost and purifying viral RNA. The extracted viruses were detected by virus isolation using embryonated chicken eggs, and the purified RNA was detected by real-time reverse transcription PCR (RRT-PCR). The virus isolation and the RRT-PCR methods were evaluated with 3 compost preparations that were produced from chicken manure mixed with corn silage, wood shavings, or wheat straw. The detection limits of both methods were 1,700 and 1,000 embryo lethal doses of AIV and NDV per gram of compost, respectively. The copy number of viral RNA quantified by RRT-PCR was highly correlated with the amount of virus in compost. The results suggested that the RRT-PCR method may be used as an alternative to the virus isolation method for rapid detection and accurate quantification of AIV and NDV in compost.

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)0.001
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 score0.749
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.108
GPT teacher head0.412
Teacher spread0.304 · 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

Citations22
Published2008
Admission routes2
Has abstractyes

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