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Record W4408140030 · doi:10.1016/j.zool.2025.126257

Maternal and social status affect hair cortisol concentrations in brown bears

2025· article· en· W4408140030 on OpenAlexaff
Massimo Franchini, Ashlee J. Mikkelsen, Agnieszka Sergiel, Nuria Selva, David M. Janz, Jonas Kindberg, Andreas Zedrosser

Bibliographic record

VenueZoology · 2025
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyAffect (linguistics)EndocrinologyCommunicationPsychology

Abstract

fetched live from OpenAlex

Hair cortisol concentration is affected by different biological factors, including age, sex, and reproductive status, and can provide important insights into fitness. Using cortisol concentrations of wild Scandinavian brown bears, Ursus arctos , the purpose of this study was to investigate cortisol variations among individuals in relation to age, sex, and reproductive status (solitary individuals vs individuals in a family group). Cortisol concentrations were measured in 448 hair samples of 303 brown bears (162 males and 141 females, from one to nine years of age) captured from 1990 to 2016. We found that members of family groups, i.e., mothers as well as their dependent offspring, had significantly higher cortisol concentrations compared to solitary individuals (males or females). Higher energetic costs in family groups are likely linked to growth of the offspring as well as maternal costs incurred by the rearing of offspring. Our findings provide valuable insights into the physiological effects of maternal care and reproductive status in mammals. • Hair cortisol is a measure of the long–term physiological response of individuals. • Higher hair cortisol concentrations in brown bear offspring and mothers. • Costs of cub rearing, growth and environmental adaptation offer explanations. • These findings enrich the still scanty information available on the Ursidae Family.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.302

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 head0.032
GPT teacher head0.342
Teacher spread0.310 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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