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Record W2090253726 · doi:10.1242/jeb.00699

Controlling muscle mitochondrial content

2003· review· en· W2090253726 on OpenAlexaff
Christopher D. Moyes

Bibliographic record

VenueJournal of Experimental Biology · 2003
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiologyActivator (genetics)Skeletal muscleMitochondrionPeroxisomeCell biologyGene expressionGeneMyocyteContext (archaeology)AnatomyBiochemistry

Abstract

fetched live from OpenAlex

Mitochondrial content, a chief determinant of aerobic capacity, varies widely among muscle types and species. Mitochondrial enzyme levels in vertebrate skeletal muscles vary more than 100-fold, from fish white muscle to bird flight muscles. Recent studies have shed light on the transcriptional regulators that control mitochondrial gene expression in muscle fiber differentiation and development, and in the context of pathological conditions such as neuromuscular disease and obesity. While the transcriptional co-activator PGC-1alpha (peroxisome proliferator-activated receptor gamma co-activator 1) has emerged as a master controller of mitochondrial gene expression, it is important to consider other mechanisms by which coordinated changes in mitochondrial content could arise. These studies, largely using biomedical models, provide important information for comparative biologists interested in the mechanistic basis of inter-species variation in muscle aerobic capacity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.103
GPT teacher head0.389
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations82
Published2003
Admission routes1
Has abstractyes

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