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Record W2047995086 · doi:10.1139/z08-083

The ability of high- and low-grooming lines of honey bees to remove the parasitic mite <i>Varroa destructor</i> is affected by environmental conditions

2008· article· en· W2047995086 on OpenAlexaffvenue
R. William Currie, Gholamhosein Tahmasbi

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiologyMiteVarroa destructorHoney BeesHoney beeAcariToxicologyVarroaEcologyHorticultureZoologyAnimal science

Abstract

fetched live from OpenAlex

This study assessed how variation in temperature and humidity affect the costs and benefits of grooming as a defense against Varroa destructor Anderson and Trueman, 2000 in high-grooming and low-grooming groups of honey bee (Apis mellifera L., 1758) workers. Grooming was quantified as the proportion of mites falling to the bottom of cages containing worker bees or to the bottom of colonies of bees during winter. Cages of 100 mite-infested bees from each line of workers were assigned to environments with three treatment combinations of temperature (10, 25, and 34 °C) and humidity (low, medium, and high), and bee and mite mortality rates were quantified. The results showed relative effectiveness of high- and low-grooming groups being affected by the environment. Differences in grooming between lines were greatest at 25 °C and were slightly higher under conditions of low humidity than at higher levels. Mite mortality rates were greater in high-grooming groups of caged bees than in low-grooming bees held at 25 and 34 °C but were similar at 10 °C. During winter, colonies with high-grooming bees had higher daily mite mortality rates than unselected colonies. Bee mortality rates were greater in high-grooming lines than in low-grooming lines under low temperatures, indicating that there may be a biological cost associated with grooming behaviour at low temperature.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 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

Citations67
Published2008
Admission routes2
Has abstractyes

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