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Record W4377819809 · doi:10.7554/elife.78335.sa2

Author response: Gene and protein expression and metabolic flux analysis reveals metabolic scaling in liver ex vivo and in vivo

2023· peer-review· en· W4377819809 on OpenAlexaff
Ngozi D. Akingbesote, Brooks P. Leitner, Daniel G. Jovin, Reina Desrouleaux, Dennis Owusu, Wanling Zhu, Zongyu Li, Michaël Pollak, Rachel J. Perry

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsIn vivoFlux (metallurgy)Metabolic flux analysisMetabolic pathwayEx vivoGene expressionScalingBiologyCytosolProteomicsMetabolismComputational biologyCell biologyChemistryBiochemistryGeneEnzymeGeneticsMathematics

Abstract

fetched live from OpenAlex

Transcriptomic, enzyme activity, and flux data demonstrate that metabolic scaling, the inverse correlation between body size and metabolic rate per gram of tissue, is a conserved phenomenon that occurs at multiple physiological levels and in multiple tissues.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.168
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1680.078

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 head0.047
GPT teacher head0.347
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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