MétaCan
Menu
← Back to cohort
Record W4323041637 · doi:10.1242/jeb.244997

Intertidal mussels survive icy Canadian winters

2023· article· en· W4323041637 on OpenAlexaffabout
Giulia S. Rossi

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsIntertidal zoneTide poolOceanographySeawaterIntertidal ecologyRocky shoreBayEcologyMytilusShoreFisheryBiologyGeology

Abstract

fetched live from OpenAlex

For many biologists, the intertidal zone is a captivating place. At low tide, seawater collects in depressions and crevasses along rocky shorelines, leaving behind tidepools teeming with life. Tidepools are often packed wall-to-wall with anemones, barnacles and snails, among other intriguing marine taxa trying to stay wet long after the tide recedes. However, not all intertidal residents are fortunate enough to secure a tidepool refuge during low tide, which is particularly problematic in temperate regions that experience bitter, cold winters. When intertidal animals are immersed in seawater, they are protected from sub-zero air temperatures. Without a tide pool refuge, however, air-exposed creatures can freeze when the tide goes out, thawing only upon immersion when the tide returns. Remarkably, many intertidal residents can survive the formation of ice crystals in their body, but how do they tolerate repeated freeze-thaw cycles? In a fascinating new study led by Lauren Gill at the University of British Columbia, a team of researchers discovered how bay mussels (Mytilus trossulus) survive repeated freezing and thawing during harsh Canadian winters.Gill and her team first collected mussels from the rugged coastline in Vancouver, Canada, to investigate their survival rate under different freezing regimes: one continuous freeze lasting 8 h, two separate freezes lasting 4 h each or four separate freezes lasting 2 h each. The researchers induced freezing in the mussels by exposing them to air at –8°C. For mussels that underwent multiple freezing bouts, the team provided a 24 h recovery in 7°C seawater between each freeze to mimic the relief that they would experience with natural tides. The researchers predicted that repeated freezing and thawing would increase mussel mortality. However, much to their surprise, survival was almost 100% in mussels frozen four separate times (4×2 h) and a mere 25% in mussels frozen only once (1×8 h).At this point, the researchers were keen to uncover the physiological mechanisms that led to such high survival in their repeatedly frozen mussels. They suspected that two different proteins could be playing an important role. The first, heat-shock protein 70 (HSP70), is known to help the body deal with extreme temperature stress. Extremely hot and cold temperatures can denature (i.e. damage) proteins in the body, but HSP70 can help protect proteins from denaturation. Sure enough, the team discovered that in repeatedly frozen mussels, the expression of HSP70 increased considerably after freezing, thereby providing mussels with protection against subsequent freezing events. The second protein is ubiquitin, which tracks down any damaged proteins in the body and flags them for disposal. Interestingly, the researchers found that in repeatedly frozen mussels, the number of proteins flagged by ubiquitin increased after freezing, indicating that the mussels were working hard to repair any molecular damage caused by freezing.Taken together, Gill and her team found that mussels have a higher survival rate when they experience repeated freeze-thaw cycles compared with a single lengthy freeze of the same total duration. Furthermore, periods of thawing are critical for surviving sub-zero temperatures because they offer mussels an opportunity to make important proteins that protect against, and repair, molecular damage caused by freezing. So, the next time you’re out exploring the captivating tidepools of the intertidal, be sure to remember the creatures outside of these pools and the great lengths that they must go to in order to survive.

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)0.000
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 score0.100
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.289
Teacher spread0.272 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental Biology→Same topicMarine Bivalve and Aquaculture Studies→French-language works237,207→