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Record W3007468898 · doi:10.1016/j.pt.2020.04.004

Varroa destructor: A Complex Parasite, Crippling Honey Bees Worldwide

2020· review· en· W3007468898 on OpenAlexfundno aff
Kirsten S. Traynor, Fanny Mondet, Joachim R. de Miranda, Maéva Angélique Techer, Vienna Kowallik, Melissa A. Y. Oddie, Panuwan Chantawannakul, Alison McAfee

Bibliographic record

VenueTrends in Parasitology · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaOkinawa Institute of Science and Technology Graduate UniversityWissenschaftskolleg zu BerlinSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungChiang Mai UniversitySvenska Forskningsrådet FormasDeutsche ForschungsgemeinschaftJapan Society for the Promotion of ScienceU.S. Department of Agriculture
KeywordsVarroaVarroa destructorHoney beeBiologyVarroa sensitive hygieneBeekeepingApis ceranaAcaricideMitePopulationBiological dispersalZoologyEcologyHoney BeesEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

The parasitic mite, Varroa destructor, has shaken the beekeeping and pollination industries since its spread from its native host, the Asian honey bee (Apis cerana), to the naïve European honey bee (Apis mellifera) used commercially for pollination and honey production around the globe. Varroa is the greatest threat to honey bee health. Worrying observations include increasing acaricide resistance in the varroa population and sinking economic treatment thresholds, suggesting that the mites or their vectored viruses are becoming more virulent. Highly infested weak colonies facilitate mite dispersal and disease transmission to stronger and healthier colonies. Here, we review recent developments in the biology, pathology, and management of varroa, and integrate older knowledge that is less well known.

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: 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
Metaresearch0.0000.000
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 science0.0000.000
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 head0.162
GPT teacher head0.424
Teacher spread0.262 · 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

Citations495
Published2020
Admission routes1
Has abstractno

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