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Record W4328048397 · doi:10.1038/s42255-023-00766-2

PCYT2-regulated lipid biosynthesis is critical to muscle health and ageing

2023· article· en· W4328048397 on OpenAlexaff
Domagoj Cikes, Kareem Elsayad, Erdinç Sezgin, Erika Koitai, Ferenc Torma, Michael Orthofer, Rebecca Yarwood, Leonhard X. Heinz, Vitaly Sedlyarov, N. Darwish, Adrian Taylor, Sophie Grapentine, Fathiya Al-Murshedi, Adelheid Weidinger, Candice Kutchukian, Colline Sanchez, Shane J. F. Cronin, Maria Novatchkova, Anoop Kavirayani, Thomas Schuetz, Bernhard J. Haubner, Lisa Haas, Astrid Hagelkrüys, Suzanne Jackowski, Andrey V. Kozlov, Vincent Jacquemond, Claude Knauf, Giulio Superti‐Furga, Eric Rullman, Thomas Gustafsson, John McDermot, Martin Lowe, Zsolt Radák, Jeffrey S. Chamberlain, Marica Bakovic, Siddharth Banka, Josef Penninger

Bibliographic record

VenueNature Metabolism · 2023
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of British ColumbiaUniversity of Guelph
FundersNational Institutes of HealthBundesministerium für Bildung, Wissenschaft und ForschungScience for Life LaboratoryKarolinska InstitutetUniversité de StrasbourgÖsterreichische ForschungsförderungsgesellschaftÖsterreichischen Akademie der WissenschaftenAgence Nationale de la RechercheAustrian Science FundEuropean CommissionNational Institute of Arthritis and Musculoskeletal and Skin DiseasesRoy J. and Lucille A. Carver College of Medicine, University of IowaSpastic Paraplegia Foundation
KeywordsFailure to thriveMuscle weaknessAgeingSkeletal muscleBiologyMuscular dystrophySarcopeniaCell biologyInternal medicineMedicineEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.336
Teacher spread0.321 · 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

Citations57
Published2023
Admission routes1
Has abstractno

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