MétaCan
Menu
Back to cohort
Record W6948356510 · doi:10.5061/dryad.qrfj6q5gx

Data from: Evolution of sociability by artificial selection

2021· dataset· en· W6948356510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPlant-based Medicinal Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMatingSelection (genetic algorithm)Biological evolutionSocial evolutionSexual selectionKin selectionInclusive fitness

Abstract

fetched live from OpenAlex

There has been extensive research on the ecology and evolution of social life in animals that live in groups. Less attention, however, has been devoted to apparently solitary species even though recent research indicates that they also possess complex social behaviors. To address this knowledge gap, we artificially selected on sociability, defined as the tendency to engage in non-aggressive activities with others, in fruit flies. Our goal was to quantify the factors that determine the level of sociability and the traits correlated with this feature. After 25 generations of selection, the high sociability lineages showed sociability scores about 50% higher than did the low sociability lineages. Experiments using the evolved lineages indicated that there were no differences in mating success between flies from the low and high lineages. Both males and females from the low lineages, however, were more aggressive than males and females from the high lineages. Finally, the evolved lineages maintained their sociability scores after ten generations of relaxed selection, suggesting no costs to maintaining low and high sociability, at least under our settings. Sociability is a complex trait, which we currently assess through genomic work on the evolved lineages.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0600.001

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 head0.405
GPT teacher head0.557
Teacher spread0.151 · 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