MétaCan
Menu
Back to cohort

Common, Emerging, Vector-Borne and Infrequent Abortogenic Virus Infections of Cattle

2011· review· en· W1820725693 on OpenAlexaff
Haytham Ali, Abdelmoneim A. Ali, Mohammad Atta, Arnost Cepica

Bibliographic record

VenueTransboundary and Emerging Diseases · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicVector-Borne Animal Diseases
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsVirusVirologyBiologyRift Valley feverVector (molecular biology)EpizooticAbortionCattle DiseasesVeterinary virologyTransmission (telecommunications)Veterinary medicineMedicinePregnancy

Abstract

fetched live from OpenAlex

This review deals with the aetiology and the diagnosis of bovine viral abortion. While the abortion rates on beef and dairy cattle farms usually do not exceed 10%, significant economic losses because of abortion storms may be encountered. Determining the cause of abortions is usually a challenge, and it generally remains obscure in more than 50% of the necropsy submitted foetuses. Bovine viral diarrhoea virus and bovine herpesvirus-1 are the most common viruses causally associated with bovine abortions in farmed cattle globally. Rift Valley fever virus and bluetongue virus are important insect-transmitted abortogenic viruses. The geographic distribution of these two viruses is primarily dependent on the distribution of the insect vector, but direct transmission is possible. Recent global warming and subsequent insect vector expansion, coupled with the increase in international trade of animals and animal products, have been important factors in recent geographic advances of those two viruses. Bovine herpesviruses-4 and 5 in cattle, as well as other less frequent vector-borne viruses including epizootic haemorrhagic disease virus, Aino virus, Wesselsbron virus and lumpy skin disease virus, are discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.284
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueTransboundary and Emerging DiseasesSame topicVector-Borne Animal DiseasesFrench-language works237,207