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Record W4408835543 · doi:10.1101/2025.03.24.644986

Multimodal social context modulates behavior in larval <i>Drosophila</i>

2025· preprint· en· W4408835543 on OpenAlexaff
Akhila Mudunuri, Élyse Zadigue-Dubé, Katrin Vogt

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsMcGill University
Fundersnot available
KeywordsDrosophila (subgenus)Context (archaeology)LarvaDrosophila melanogasterBiologyNeuroscienceEcologyGeneticsGene

Abstract

fetched live from OpenAlex

All animals need to navigate and make decisions in social environments. They influence each other's behavior, but how important this is and how they process and represent social information in their brain is less well understood. This includes fruit flies and fly larvae, which are usually not known as social insects. Using a Drosophila larva assay with reduced stimulation, we found that larval groups show enhanced dispersal and distance from each other in the absence of food. This social context-dependent modulation overrides responses to other external sensory cues and is shaped by developmental social experience. Leveraging the genetic toolbox available in Drosophila , we find that different sensory modalities are required for normal social context modulation. Our results show that even less social animals like fly larvae are affected by conspecifics and that they recognize each other through multimodal sensory cues. This study provides a tractable system for future dissection of the neural circuit mechanisms underlying social interactions.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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