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Record W1687567765 · doi:10.1139/h09-023

Molecular responses to strength and endurance training: Are they incompatible?This paper article is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.

2009· review· en· W1687567765 on OpenAlexvenueno aff
John A. Hawley

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersAustralian Sports CommissionGlaxoSmithKline
KeywordsEndurance trainingTraining (meteorology)Adaptation (eye)Selection (genetic algorithm)Strength trainingMode (computer interface)Resistance trainingPsychologyNeuroscienceComputer sciencePhysical medicine and rehabilitationMedicinePhysical therapyHuman–computer interactionArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Simultaneously training for both strength and endurance results in a compromised adaptation, compared with training for either exercise mode alone. This has been variously described as the concurrent training effect or the interference effect. It now appears that the genetic and molecular mechanisms of adaptation induced by resistance- and endurance-based training are distinct, with each mode of exercise activating and (or) repressing specific subsets of genes and cellular signalling pathways. This brief review will summarize our current understanding of the molecular responses to strength and endurance training, and will examine the molecular evidence for an interference effect when concurrent training is undertaken. A better understanding of the activation and interaction of the molecular pathways in response to these different modes of exercise will permit sport scientists to develop improved training programs capable of maximizing both strength and endurance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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 designBench or experimental
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

Citations197
Published2009
Admission routes1
Has abstractyes

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