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Record W4416590047 · doi:10.1080/15230430.2025.2572150

Living in ice: Examining the effects of temperature on thermal and metabolic physiology of glacier ice worms ( <i>Mesenchytraeus solifugus</i> )

2025· article· en· W4416590047 on OpenAlexaff
Tristenne Cranford, Scott Hotaling, Peter Wimberger, Susannah Hannaford, Katie E. Marshall, Rachel L. Malison

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlacierCryosphereSnowIce-albedo feedbackFreezing pointMetabolic rateSnowpackClimate changeCold climate

Abstract

fetched live from OpenAlex

Animals employ many strategies to survive in extreme cold. Glacier ice worms (Mesenchytraeus solifugus) are the largest animals that spend their entire life cycle in ice. Indeed, they spend most of their lives near 0°C and accumulate adenosine trisphosphate (ATP) to mitigate the impacts of cold on their performance. However, the degree to which ice worms can survive temperatures above and below freezing has not been thoroughly investigated. Furthermore, the role of cold temperatures in shaping whole-body metabolism in ice worms is largely unknown. Here, we assessed thermal limits of ice worms as well as their whole-body metabolic rates. Notably, we found that though ice worms can survive short-term exposures to surprisingly warm temperatures (~26°C), they cannot tolerate freezing, including internal ice formation. Ice worm metabolic rates also significantly increased with temperature up to 16°C where a significant break point occurred. Taken together, our results further illuminate how ice worms survive their unique life in ice and highlight how close they live to their lower thermal limits. Looking ahead, we note the clear risks that anthropogenic climate change, glacier recession, and loss of mountain snowpack pose to the future of ice worms.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.285
Teacher spread0.267 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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