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Record W3088699658 · doi:10.1042/cs20201028

The dual nature of obesity in metabolic programming: quantity versus quality of adipose tissue

2020· article· en· W3088699658 on OpenAlexafffund
Anissa Viveiros, Gavin Y. Oudit

Bibliographic record

VenueClinical Science · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsAdipose tissueObesityContext (archaeology)OffspringChildhood obesityBioinformaticsBiologyPhenotypeEndocrinologyInternal medicineMedicineOverweightGeneGeneticsPregnancy

Abstract

fetched live from OpenAlex

The global prevalence of obesity has been rising at an alarming rate, accompanied by an increase in both childhood and maternal obesity. The concept of metabolic programming is highly topical, and in this context, describes a predisposition of offspring of obese mothers to the development of obesity independent of environmental factors. Research published in this issue of Clinical Science conducted by Litzenburger and colleagues (Clin. Sci. (Lond.) (2020) 134, 921-939) have identified sex-dependent differences in metabolic programming and identify putative signaling pathways involved in the differential phenotype of adipose tissue between males and females. Delineating the distinction between metabolically healthy and unhealthy obesity is a topic of emerging interest, and the precise nature of adipocytes are key to pathogenesis, independent of adipose tissue volume.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.132
GPT teacher head0.463
Teacher spread0.331 · 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 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

Citations7
Published2020
Admission routes2
Has abstractyes

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