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Record W3168801160 · doi:10.1101/627729

Honey bee queens are vulnerable to heat-induced loss of fertility

2019· preprint· en· W3168801160 on OpenAlexafffund
Alison McAfee, Abigail Chapman, Heather Higo, Robyn M. Underwood, Joseph Milone, Leonard J. Foster, M. Marta Guarna, David R. Tarpy, Jeffery S. Pettis

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Food and AgricultureGenome British ColumbiaGenome CanadaU.S. Department of Agriculture
KeywordsBiologyBiomonitoringSpermathecaSpermHoney beeHeat stressHeat shock proteinEcologyFertilityToxicologyZoologyPopulationBotanyAnimal scienceEnvironmental healthGenetics

Abstract

fetched live from OpenAlex

Abstract All species need to reproduce to maintain viable populations, but heat stress kills sperm cells across the animal kingdom and rising frequencies of heatwaves are a threat to biodiversity. Honey bees ( Apis mellifera ) are globally distributed micro-livestock; therefore, they could serve as environmental biomonitors for fertility losses. Here, we found that queens have two potential routes of temperaturestress exposure: within colonies and during routine shipping. Our data suggest that temperatures of 15 to 38°C are safe for queens at a tolerance threshold of 11.5% loss of sperm viability, which is the viability difference associated with queen failure in the field. Heat shock activates expression of specific stressresponse proteins in the spermatheca, which could serve as molecular biomarkers (indicators) for heat stress. This protein fingerprint may eventually enable surveys for the prevalence of heat-induced loss of sperm viability in diverse landscapes as part of a biomonitoring program.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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