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Record W3153476930 · doi:10.1038/s42003-021-01887-8

The impact of locomotion on the brain evolution of squirrels and close relatives

2021· article· en· W3153476930 on OpenAlexafffund
Ornella Bertrand, Hans P. Püschel, Julia A. Schwab, Mary Silcox, Stephen L. Brusatte

Bibliographic record

VenueCommunications Biology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersH2020 Marie Skłodowska-Curie ActionsAgencia Nacional de Investigación y DesarrolloCHIST-ERANatural Sciences and Engineering Research Council of CanadaEuropean CommissionLeverhulme TrustHorizon 2020 Framework ProgrammeAgenția Națională pentru Cercetare și DezvoltareMcGill UniversityAmerican Museum of Natural History
KeywordsEndocastFossorialBiologyNeocortexExtant taxonBrain sizePhylogeneticsEvolutionary biologyArboreal locomotionZoologyEcologyNeuroscienceAnatomySkull

Abstract

fetched live from OpenAlex

How do brain size and proportions relate to ecology and evolutionary history? Here, we use virtual endocasts from 38 extinct and extant rodent species spanning 50+ million years of evolution to assess the impact of locomotion, body mass, and phylogeny on the size of the brain, olfactory bulbs, petrosal lobules, and neocortex. We find that body mass and phylogeny are highly correlated with relative brain and brain component size, and that locomotion strongly influences brain, petrosal lobule, and neocortical sizes. Notably, species living in trees have greater relative overall brain, petrosal lobule, and neocortical sizes compared to other locomotor categories, especially fossorial taxa. Across millions of years of Eocene-Recent environmental change, arboreality played a major role in the early evolution of squirrels and closely related aplodontiids, promoting the expansion of the neocortex and petrosal lobules. Fossoriality in aplodontiids had an opposing effect by reducing the need for large brains.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.461

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 studies0.0000.001
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 head0.048
GPT teacher head0.324
Teacher spread0.276 · 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

Citations50
Published2021
Admission routes2
Has abstractyes

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