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Record W2012774229 · doi:10.1155/2012/809576

Exercise Intensity Modulation of Hepatic Lipid Metabolism

2012· article· en· W2012774229 on OpenAlexaff
Fábio Santos Lira, Luiz Carlos Carnevali, Nelo Eidy Zanchi, Ronaldo Vagner Thomatieli dos Santos, Jean Marc Lavoie, Marília Seelaender

Bibliographic record

VenueJournal of Nutrition and Metabolism · 2012
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLipid metabolismKetone bodiesBeta oxidationVery low-density lipoproteinMedicineMetabolismEndocrinologyFatty acid metabolismInternal medicineAerobic exerciseFatty acid synthesisSecretionFatty acidChemistryBiochemistryLipoproteinCholesterol

Abstract

fetched live from OpenAlex

Lipid metabolism in the liver is complex and involves the synthesis and secretion of very low density lipoproteins (VLDL), ketone bodies, and high rates of fatty acid oxidation, synthesis, and esterification. Exercise training induces several changes in lipid metabolism in the liver and affects VLDL secretion and fatty acid oxidation. These alterations are even more conspicuous in disease, as in obesity, and cancer cachexia. Our understanding of the mechanisms leading to metabolic adaptations in the liver as induced by exercise training has advanced considerably in the recent years, but much remains to be addressed. More recently, the adoption of high intensity exercise training has been put forward as a means of modulating hepatic metabolism. The purpose of the present paper is to summarise and discuss the merit of such new knowledge.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.024
GPT teacher head0.282
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations48
Published2012
Admission routes1
Has abstractyes

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