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LIMITS TO FAT OXIDATION BY SKELETAL MUSCLE DURING EXERCISE

2001· article· en· W2034941969 on OpenAlexaff
Lawrence L. Spriet, John A. Hawley, Louise M. Burke, Jørn Wulff Helge, Mark Hargreaves

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2001
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSkeletal muscleInternal medicinePhysical medicine and rehabilitationMedicineChemistryEndocrinologyPhysical therapy

Abstract

fetched live from OpenAlex

Compared with the body's stores of carbohydrate, endogenous fat depots are large and represent a potentially unlimited source of fuel for oxidation by skeletal muscle during aerobic exercise. However, fatty acid (FA) oxidation by muscle is limited, especially at the exercise intensities sustained by athletes during training and competition. This symposium will focus on nutritional interventions that promote FA oxidation, attenuate the rate of muscle glycogen utilization and modify subsequent exercise capacity and detail the mechanisms that underlie such perturbations. Dr. Spriet will provide an overview of the regulation of FA oxidation by skeletal muscle during exercise. Dr. Hawley will then discuss the impact of altering FA availability on substrate utilization and exercise performance. Dr. Burke and Dr. Helge will present data on the effects of both short-term (<7d) and long-term (>7d) adaptation to a high-fat diet on metabolism, training capacity and performance during endurance and ultra-endurance exercise. Finally, Dr. Hargreaves will discuss the effects of a high-fat diet on gene expression in skeletal muscle. This symposium will highlight the results of new investigations that have utilized various nutritional strategies to increase FA oxidation by muscle during exercise, as well as providing a state-of-the-art synopsis of our current knowledge of the regulation of FA oxidation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.301
Teacher spread0.280 · 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.

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

Citations1
Published2001
Admission routes1
Has abstractyes

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