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Record W3175459900 · doi:10.60692/fw26s-hp417

Publisher Correction: Methylation deficiency disrupts biological rhythms from bacteria to humans

2020· article· en· W3175459900 on OpenAlexaff
Jean‐Michel Fustin, Shiqi Ye, Christin Rakers, Kensuke Kaneko, Mayu Yamano, Marijke Versteven, Ellen Grünewald, Samantha J. Cargill, T. Katherine Tamai, Yao Xu, Maria Luísa Jabbur, Rika Kojima, Melisa L. Lamberti, Kumiko Yoshioka-Kobayashi, David Whitmore, Stephanie Tammam, P. Lynne Howell, Ryoichiro Kageyama, Takuya Matsuo, Ralf Stanewsky, Diego A. Golombék, Carl Hirschie Johnson, Hideaki Kakeya, Gerben van Ooijen, Hitoshi Okamura

Bibliographic record

VenueChesterRep (University of Chester) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsRhythmBiologyBacteriaMethylationCommunicationNeuroscienceComputational biologyPsychologyMedicineGeneticsInternal medicineGene

Abstract

fetched live from OpenAlex

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.994
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0700.032

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 head0.034
GPT teacher head0.232
Teacher spread0.198 · 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.

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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