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Record W4390660554 · doi:10.1101/2024.01.05.574416

Climate change, age acceleration, and the erosion of fitness in polar bears

2024· preprint· en· W4390660554 on OpenAlexafffund
Levi Newediuk, Evan S. Richardson, Brooke A. Biddlecombe, Haziqa Kassim, Leah Kathan, Nicholas J. Lunn, L. Ruth Rivkin, Ola E. Salama, Chloé Schmidt, Meaghan J. Jones, Colin J. Garroway

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaChurchill Northern Studies CentreWorld Wildlife FundParks CanadaUniversity of Manitoba
KeywordsArcticClimate changeAccelerationEpigeneticsPopulationBiologyGlobal warmingLife history theoryAdaptive responseEcologyLife historyEnvironmental scienceGeographyDemographyGenetics

Abstract

fetched live from OpenAlex

Abstract Climate change is increasingly disrupting evolved life history strategies and reducing population viability in wild species. Using estimates of epigenetic age acceleration, a cellular biomarker of lifetime stress and the expression of age-related phenotypes, we found that polar bears aged approximately one year faster for each degree of warming since the 1960s. Age acceleration was also associated with reproducing early in life, linking this cellular process to well-established life history theory. However, we found evidence for the erosion of fitness as epigenetic aging accelerated and temperatures increased. Finally, using a large pedigree, we found adaptive potential in our study population was approximately zero. Global temperatures will soon reach the levels of warming currently experienced by Arctic species, which could impose widespread physiological costs and limit adaptive capacities worldwide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicMarine animal studies overview→French-language works237,207→