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Record W1986184875 · doi:10.1080/03079457.2014.921279

Investigation into the possibility of vertical transmission of avian bornavirus in free-ranging Canada geese ( <i>Branta canadensis</i> )

2014· article· en· W1986184875 on OpenAlexafffundabout
Pauline Delnatte, Éva Nagy, Davor Ojkić, Graham J. Crawshaw, Dale A. Smith

Bibliographic record

VenueAvian Pathology · 2014
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of GuelphToronto Zoo
FundersOVC Pet Trust
KeywordsBiologyBrantaGooseIn ovoHatchlingEmbryoYolkYolk sacVirusWaterfowlVirologyAnimal scienceEcologyGeneticsHabitat

Abstract

fetched live from OpenAlex

To investigate the possibility of in ovo infection with avian bornavirus (ABV) in wild Canada geese (Branta canadensis), 53 eggs were opportunistically collected at various stages of embryonic development from 16 free-ranging goose nests at a large urban zoo site where ABV infection is known to be present in this species. ABV RNA was detected in the yolk of one of three unembryonated eggs using real-time reverse transcription polymerase chain reaction. ABV RNA was not identified in the brains from 23 newly hatched goslings or 19 embryos, nor from three early whole embryos. Antibodies against ABV were not detected in the plasma of any of the hatched goslings using an enzyme-linked immunosorbent assay. Possible reasons for the failure to detect ABV RNA in hatchlings or embryos include low sample size, eggs deriving from parents not actively infected with ABV, the testing of only brain tissue, and failure of the virus to replicate in Canada goose embryos. In conclusion, this preliminary investigation demonstrating the presence of ABV RNA in the yolk of a Canada goose egg provides the first evidence for the potential for vertical transmission of ABV in waterfowl.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

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 head0.240
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2014
Admission routes3
Has abstractyes

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