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Record W2942683994 · doi:10.1371/journal.pbio.3000256

Feminizer and doublesex knock-outs cause honey bees to switch sexes

2019· letter· en· W2942683994 on OpenAlexaff
Alison McAfee, Jeffery S. Pettis, David R. Tarpy, Leonard J. Foster

Bibliographic record

VenuePLoS Biology · 2019
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDoublesexBiologyHoney beeCRISPRDroneEvolutionary biologyGeneticsZoologyGene

Abstract

fetched live from OpenAlex

Honey bees are experts at refuting societal norms. Their matriarchal hives are headed by queens, backed by an all-female workforce, and males die soon after copulation. But the biochemical basis of how these distinct castes and sexes (queens, workers, and drones) arise is poorly understood, partly due to a lack of efficient tools for genetic manipulation. Now, Roth and colleagues have used clustered regularly interspaced short palindromic repeats (CRISPR) to knock out two key genes (feminizer and doublesex) that guide sexual development. Their technique yielded remarkably low rates of genetic mosaicism and offers a promising tool for engineering and phenotyping bees for diverse applications.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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.

Study designNot applicable
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

Citations29
Published2019
Admission routes1
Has abstractyes

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