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Record W2795188980

Avian Pox in Shearwaters on Lord Howe Island

2017· article· en· W2795188980 on OpenAlexaff
Subir Sarker, Shubhagata Das, Jennifer L. Lavers, Ian Hutton, Karla J. Helbig, Chris Upton, Jacob J. Imbery, Shane Raidal

Bibliographic record

VenueCharles Sturt University Research Output (CRO) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFisheryGeographyHistoryAncient historyBiology
DOInot available

Abstract

fetched live from OpenAlex

Avipoxvirus infections occur in a wide range of bird species worldwide. In Australia pox is common in the Australian magpie (Cracticus tibicen), Currawongs (Strepera spp.) and Silvereyes (Zosterops lateralis) but very little is known about the evolution of this family of viruses or the disease ecology of avian poxviruses in seabirds. Pox lesions have been seen in colonies of Shy Albatross (Thalassarche cauta) in Bass Strait but the epidemiology of pox in pelagic birds is not very well elucidated. Two novel avipoxvirus species demonstrated in Flesh-footed Shearwater (A. carneipes) and Wedge-tailed Shearwater (A. pacificus) (SWPV-2) recently discovered in birds from Lord Howe Island had relatively close relationships with Canarypox virus, particularly within the highly conserved polymerase gene of these viruses. This raised some concern regarding the potential for cosmopolitan pathogens to spill over into wildlife. However, the results highlight how mistakes in interpretation could occur if only highly conserved genes are used to detect and or characterise viral infections. The results also contribute to a deeper understanding of the genetic relationships and likely complex epidemiology among avipoxvirus species in wildlife species.

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)0.001
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.141
GPT teacher head0.380
Teacher spread0.239 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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