Editorial Note - An assessment of optimizing biofuel yield percentage using K-fold integrated machine learning models for a sustainable future

Sep 29, 2026

Upload by jay

This document serves as an official Editorial Note issued by the PLOS One editorial team regarding a previously published article titled 'An assessment of optimizing biofuel yield percentage using K-fold integrated machine learning models for a sustainable future'. The note addresses formal concerns identified by the editors concerning the article's compliance with established PLOS authorship policies. The editorial team expresses regret that these specific authorship issues were not adequately identified or resolved prior to the original publication of the research paper in early 2025. This notice acts as a formal record of the discrepancy, ensuring transparency for readers regarding the authorship integrity of the work involving machine learning applications in biofuel production. The note was published in PLOS One on January 6, 2026, to ensure that the scientific community remains informed about the adherence to academic publication standards and the verification of author contributions in complex research projects.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Editorial Note Regarding Authorship Compliance

Reference Information

FAQs

What is the purpose of this editorial note?

The purpose is to inform readers about concerns regarding the compliance of the article 'An assessment of optimizing biofuel yield percentage...' with the PLOS Authorship policy.

When was this editorial note published?

This editorial note was published on January 6, 2026.

What specific issue does the editorial note address?

It addresses issues related to compliance with PLOS Authorship policy that were not resolved before the original article's publication.

Sep 29, 2026

Upload by jay