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Record W4408170081 · doi:10.1038/s41597-025-04724-3

Multifaceted and extensive behavioral trajectories of genomically diverse Drosophila lines

2025· article· en· W4408170081 on OpenAlexfundno aff
Daiki X. Sato, タケオ オクヤマ, Yuma Takahashi

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsnot available
FundersInstitute of GeneticsACT-XJapan Science and Technology AgencyJapan Society for the Promotion of ScienceJapan Science Society
KeywordsDrosophila (subgenus)Computational biologyEvolutionary biologyComputer scienceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Detailed tracking data is essential to understanding the intricate mechanisms behind animal behavior. Here, we present a comprehensive dataset containing behavioral movies and trajectories from over 30,000 Drosophila melanogaster individuals across 105 genetically distinct strains, including 104 wild-type strains from the Drosophila Genetic Reference Panel, along with one visually impaired mutant. These data, categorized by genetic background, sex, and social context (isolated or in groups), were collected during 15-minute sessions that included five minutes of repeated looming stimuli to elicit fear responses. Additionally, our experimental design incorporated group experiments with randomly combined pairs of strains to investigate synergistic effects of group members on behavioral dynamics. Beyond enabling detailed analyses of genetic factors underlying locomotion, fear responses, and social interactions, this dataset provides a unique opportunity to examine individual behavioral variability within genetically identical flies. By capturing a broad spectrum of behaviors across different genetic and environmental contexts, these data serve as a valuable resource for advancing our understanding of how genetics, individuality, and group interactions shape animal behavior.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.114
GPT teacher head0.371
Teacher spread0.257 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes1
Has abstractyes

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