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Record W4415909341 · doi:10.1098/rsbl.2025.0564

Naked mole-rats employ a normoxic escape behaviour that is altered by social interaction

2025· article· en· W4415909341 on OpenAlexafffund
Pareesa Lashani, Gloria Lamontagne, Karen L. Kadamani, Matthew E. Pamenter

Bibliographic record

VenueBiology Letters · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSocialityHypoxia (environmental)Escape responseSocial relationPreferenceForaging

Abstract

fetched live from OpenAlex

Escape behaviours are a common response when animals encounter hypoxic environments. Confoundingly, naked mole-rats (NMRs) experience hypoxia while sleeping in crowded colony nest chambers, from which escape may not be desirable. In isolation, individual NMRs decrease physical activity to save energy in hypoxia, but this response is absent when conspecifics are present. However, whether NMRs try to escape hypoxia is unknown, as is the impact of sociality on any hypoxic escape behaviours. We predicted that individual NMRs would try to escape from hypoxic environments, but that sociality would reduce the drive to escape. We allowed individual and paired NMRs to choose between normoxia (21% O₂) or various depths of hypoxia (3%, 7% or 11% O₂) and non-invasively recorded their activity. Surprisingly, individual NMRs exhibited a novel normoxic escape behaviour and preferred severe hypoxia (3% O 2 ) to a normoxic environment. This preference was not repeated in less severe levels of hypoxia. Paired animals also preferred a hypoxic environment over normoxia, but social interactions drove an increase in movement velocity and reduced the severity of their preferred level of environmental hypoxia to 7% O 2 . Thus, NMRs choose hypoxic environments over normoxic environments and sociality impacts this behavioural choice in hypoxia.

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 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 score0.056
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.045
GPT teacher head0.323
Teacher spread0.278 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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