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Record W3080342915 · doi:10.3389/fphys.2020.00864

Corrigendum: Every-Other-Day Feeding Decreases Glycolytic and Mitochondrial Energy-Producing Potentials in the Brain and Liver of Young Mice

2020· erratum· en· W3080342915 on OpenAlexaff
Oksana M. Sorochynska, Maria M. Bayliak, Dmytro V. Gospodaryov, Yulia V. Vasylyk, Oksana V. Kuzniak, Tetiana M. Pankiv, Olga Garaschuk, Kenneth B. Storey, Volodymyr I. Lushchak

Bibliographic record

VenueFrontiers in Physiology · 2020
Typeerratum
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsKetone bodiesMistakeRegimenDehydrogenaseEnergy metabolismEndocrinologyInternal medicineMedicineBiologyEnzymeMetabolismBiochemistry

Abstract

fetched live from OpenAlex

In the original article, there was a mistake in Figure 6 as published. The incorrect data on the level of ketone bodies in the liver and the cortex of mice fed ad libitum (control) or subjected to an every-other-day feeding regimen (EODF) over 1 month were placed instead of the corresponding data on β-hydroxybutyrate dehydrogenase activity. The error was introduced during the publishing process, and the correct figure was in the author's proofs. The corrected Figure 6 and the legend appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.The activity of β-hydroxybutyrate dehydrogenase (HBDH) in the liver (A) and the cortex (B) of mice fed ad libitum (control) or subjected to an every-other-day feeding regimen (EODF) over 1 month, n = 5-6 mice. *Significantly different from the control group (p < 0.05), # significantly different from corresponding group of males (p < 0.05) by Welch's t test with Benjamini-Hochberg adjustment of p.

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)0.010
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: Other · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0490.029

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 head0.016
GPT teacher head0.241
Teacher spread0.224 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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