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Record W2172764802 · doi:10.1139/cjz-2015-0027

Seasonal oxidative capacity of skeletal muscles in hibernating Daurian ground squirrels (<i>Spermophilus</i> <i>dauricus</i>)

2015· article· en· W2172764802 on OpenAlexvenueno aff
Shanfeng Jiang, Shupan Guo, Wei Xue, Huiping Wang, Nandu Goswami, Yunfang Gao

Bibliographic record

VenueCanadian Journal of Zoology · 2015
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
Fundersnot available
KeywordsCitrate synthaseHibernation (computing)BiologyInternal medicineGround squirrelEndocrinologyMyoglobinLactate dehydrogenaseSkeletal muscleOxidative phosphorylationBiochemistryEnzymeMedicine

Abstract

fetched live from OpenAlex

We investigated the mechanism of high oxidative capacity of skeletal muscles in hibernating Daurian ground squirrels (Spermophilus dauricus Brandt, 1843). Myoglobin (Mb) levels, as well as citrate synthase and lactate dehydrogenase (LDH) activities, were measured by spectrophotometry. Mb content in the soleus (SOL) muscle lasted from the beginning of hibernation to spring. Mb content in SOL was 87% higher in the hibernating group than in the summer group. Mb content in the extensor digitorum longus (EDL) muscle stayed at similar levels during the different periods of the year. Citrate synthase activity in SOL was 30% higher in the hibernating group than in the summer group. Meanwhile, citrate synthase activity in EDL did not change during hibernation. LDH activity in SOL was not different between the hibernating group and the summer active group, whereas LDH activity in EDL increased significantly (up to 11%) in the 2 days arousal after hibernation group compared with the hibernating group. We conclude that high oxidative capacity is provided by increased oxygen storage capacity of slow-twitch muscle fibers rather than from fast-twitch muscle fibers in hibernating animals.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.257
Teacher spread0.228 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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