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Record W1863750550 · doi:10.1159/000329281

Metabolic Regulation of Fat Use during Exercise and in Recovery

2011· article· en· W1863750550 on OpenAlexaff
Lawrence L. Spriet

Bibliographic record

VenueNestlé Nutrition Institute Workshop series · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAerobic exerciseCompartmentalization (fire protection)ChemistryInternal medicineVO2 maxEndocrinologyMedicineBiochemistryHeart rate

Abstract

fetched live from OpenAlex

Fat is an important fuel for exercise but plays a secondary role to carbohydrate (CHO). Increasing fat use during exercise can decrease the reliance on CHO and spare CHO for later in training sessions or competitions that depend on CHO for success. The pathways that metabolize and oxidize fat are activated more slowly than CHO at the onset of exercise and reach a maximum at moderate exercise intensities. As exercise intensity increases to ∼75% VO(2max) and beyond, fat metabolism is inhibited: using CHO will increase the amount of energy produced per liter of oxygen consumed. The capacity for fat use during exercise is increased by aerobic training and the dietary combination of little or no CHO intake and high fat intake. Fat oxidation is very dependent on the mitochondrial volume of muscle but other key sites of regulation include release of fat from storage forms and fat transport across plasma and mitochondrial membranes. This chapter examines the control of fat metabolism during moderate and intense exercise with an emphasis on human findings and the adaptations that occur with aerobic training and other acute nutritional manipulations. Recent work using molecular and cellular compartmentalization techniques have advanced the knowledge in this area.

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 score0.399
Threshold uncertainty score0.619

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 head0.023
GPT teacher head0.226
Teacher spread0.203 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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