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Record W4402485113 · doi:10.1016/j.isci.2024.110906

Differences in honeybee queen pheromones revealed by LC-MS/MS: Reassessing the honest signal hypothesis

2024· article· en· W4402485113 on OpenAlexafffund
Alison McAfee, Armando Alcázar Magaña, Leonard J. Foster, Shelley E. Hoover

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of LethbridgeCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundLife Sciences InstituteGenome British ColumbiaCanada Foundation for Innovation
KeywordsSex pheromoneQueen (butterfly)Honey beeChemistryBiologyBotanyHymenoptera

Abstract

fetched live from OpenAlex

The honest signal hypothesis posits that social insect queens emit pheromonal signals that convey information about fecundity, which workers use to make decisions around investing in direct or indirect fitness. We used liquid chromatography-tandem mass spectrometry to measure honeybee ( Apis mellifera ) queen retinue pheromone (QRP) in relation to age, laying status, and acceptance using a protocol that enables concurrent metabolomic and lipidomic analyses. Older queens produced higher levels of the QRP components 9-R-hydroxydec-2(E)-enoic acid (9(R)-HDA), linolenic acid (LEA), and 4-hydroxy-3-methoxyphenylethanol (HVA) compared to younger queens, with HVA also correlating with ovary mass. However, ovary mass was not an influential metric for worker decision-making around queen acceptance; therefore, the relationship between HVA and ovary mass is merely an "honest signal" of a non-influential metric. Parallel metabolomic and lipidomic analyses showed that samples cluster according to queen age and mating status, but not ovary mass, revealing many other physiological changes occurring in the queen's early life.

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 score0.853
Threshold uncertainty score0.324

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 studies0.0000.000
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 head0.056
GPT teacher head0.223
Teacher spread0.167 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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