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Record W4394961959 · doi:10.1038/s41556-024-01417-8

Author Correction: Cancer-associated fibroblast-derived acetate promotes pancreatic cancer development by altering polyamine metabolism via the ACSS2–SP1–SAT1 axis

2024· erratum· en· W4394961959 on OpenAlexaff
Divya Murthy, Kuldeep S. Attri, Surendra K. Shukla, Ravi Thakur, Nina V. Chaika, Chunbo He, Dezhen Wang, Kanupriya Jha, Aneesha Dasgupta, Ryan J. King, Scott E. Mulder, Joshua J. Souchek, Teklab Gebregiworgis, Vikant Rai, Rohit Patel, Tuo Hu, Sandeep Rana, Sai Sundeep Kollala, Camila Pacheco, Paul M. Grandgenett, Fang Yu, Vikas Kumar, Audrey J. Lazenby, Adrian R. Black, Susanna Ulhannan, Ajay Jain, Barish H. Edil, David Klinkebiel, Robert Powers, Amarnath Natarajan, Michael A. Hollingsworth, Kamiya Mehla, Quan P. Ly, Sarika Chaudhary, Rosa F. Hwang, Kathryn E. Wellen, Pankaj K. Singh

Bibliographic record

VenueNature Cell Biology · 2024
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsPancreatic cancerCell biologyPolyamineChemistryFibroblastCancer cellCancerBiologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

In the version of this article initially published, the surname of Susanna Ulahannan was misspelled (Ulhannan); in Extended Data Fig. 5b, a representative blot used to confirm knockdown efficiency for the CFPAC-1 was also used in Extended Data Fig. 9h; and in Extended Data Fig. 5j, both images in shACSS2-A+HPS and shACSS2-B (without HPS) were from the same tissue; the ACSS2 IHC panel (top panel) has been replaced. The name and figure have been updated in the HTML and PDF versions of the article. For comparison, the original Extended Data Fig. 5 is available as Supplementary Information accompanying this amendment.

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)0.003
metaresearch head score (Gemma)0.028
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 score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0360.027

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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