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Record W4309603933 · doi:10.3390/app122211630

Effects of Varroa destructor on Hemolymph Sugars and Secondary Infections in Honeybees (Apis mellifera)

2022· article· en· W4309603933 on OpenAlexaff
Antoine Cournoyer, Laurence Plamondon, Liza Bau‐Gaudreault, Annie Deschamps, P. Dubreuil, Marie‐Odile Benoit‐Biancamano

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsCanadian Nuclear LaboratoriesUniversité LavalUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsVarroa destructorVarroaHemolymphBiologyDeformed wing virusVeterinary medicineMiteHorticultureBotanyMedicine

Abstract

fetched live from OpenAlex

The European honeybee contributes to the agriculture by its pollination; however, the overwintering loss rate over the last decades is worrisome. Varroa destructor is considered one of the most important causes of bee colony declines. This project aims to correlate the infestation by varroa to the hemolymph sugar concentrations and bacterial and viral coinfections. Six highly infested and six control hives were compared over time. Pooled hemolymph samples from honeybees were collected for sugar concentration measurements using a previously validated portable glucometer. The hemolymph samples were submitted for bacteriology. Multiplex RT-PCR analysis was performed on honeybees for six viruses: DWV-A, DWV-B, BQCV, ABPV, KBV, and IAPV. There was also no predominance of pathogenic bacteria. In September, sugar concentrations in hemolymph were significantly lower in highly infested hives than in control hives. Infested hives showed markedly higher viral loads except for ABPV. DWV-A and BQCV viral loads from highly infested hives were significantly higher in September compared to July. A continued and severe exposure to varroa leads to increased viral charges and decreased sugar concentrations, suggesting alterations in immunity, metabolism, and reserve mobilization. These parameters contribute to the weakening and mortality of the colonies.

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)0.000
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 score0.697
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.227
Teacher spread0.214 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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