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Record W1878699965 · doi:10.1016/j.febslet.2015.10.026

Low‐temperature microRNA expression in the painted turtle, <i>Chrysemys picta</i> during freezing stress

2015· article· en· W1878699965 on OpenAlexafffund
Kyle K. Biggar, Kenneth B. Storey

Bibliographic record

VenueFEBS Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPainted turtleTurtle (robot)ChemistrymicroRNACell biologyBiologyEcologyBiochemistryGene

Abstract

fetched live from OpenAlex

Natural freeze tolerance depends on cellular adaptations that address the multiple stresses imposed on cells during freezing. These adaptations preserve viability by suppressing energy-expensive cell processes in the frozen state. In this study, we explore the freeze-responsive expression of microRNA in hatchling painted turtles exposed to 20 h freezing. Furthermore, we also explore the possibility of unique temperature-sensitive microRNA targeting programs that aid in adapting turtles for survival in the frozen state. Interestingly, two freeze-responsive 'cryo-miRs' (cpm-miR-16 and cpm-miR-21) were found to have unique low-temperature mRNA targets enriched in biological processes that are known to be part of the stress response.

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 score0.059
Threshold uncertainty score0.275

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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