There are errors in the author affiliations. The correct affiliations are as follows:
Fengwei An1,2, Haoran Jiang3, Yulong Zhao4, Lei Fang5, Shujing Dong4, Wenpeng Du3, Guohua Wu4, Wei Liu1, Zhenglong Lv1, Hao Liang4
1 JSTI GROUP Co., Ltd., Nanjing, China, 2 National Engineering Research Center for Advanced Road Materials, Nanjing, China, 3 Shandong Pengcheng Road and Bridge Group Co., Ltd., Jining, China, 4 School of Transportation and Civil Engineering, Shandong Jiaotong University, Jinan, China, 5 Jining-Jizhou Expressway Co., Ltd., Jining, China.
Reference
Citation: An F, Jiang H, Zhao Y, Fang L, Dong S, Du W, et al. (2026) Correction: Binary image acquisition and texture parameter calculation of asphalt pavement based on a U-Net model. PLoS One 21(8): e0357118. https://doi.org/10.1371/journal.pone.0357118
Published: August 26, 2026
Copyright: © 2026 An et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Facts Only
Fengwei An, Haoran Jiang, Yulong Zhao, Lei Fang, Shujing Dong, Wenpeng Du, Guohua Wu, Wei Liu, and Zhenglong Lv are listed with affiliations at JSTI GROUP Co., Ltd., National Engineering Research Center for Advanced Road Materials, Shandong Pengcheng Road and Bridge Group Co., Ltd., School of Transportation and Civil Engineering, Shandong Jiaotong University, and Jining-Jizhou Expressway Co., Ltd.
The cited reference is: An F, Jiang H, Zhao Y, Fang L, Dong S, Du W, et al. (2026) Correction: Binary image acquisition and texture parameter calculation of asphalt pavement based on a U-Net model. PLoS One 21(8): e0357118.
The publication date is August 26, 2026.
Executive Summary
Full Take
The context involves the dissemination of specific technical research regarding asphalt pavement analysis using a U-Net model. The pattern observed is that rigorous academic work, when presented with corrected affiliations and clear licensing, establishes a baseline of verifiable fact. The implication here touches upon the relationship between institutional recognition (affiliations) and the credibility of scientific findings. A potential tension lies in ensuring that attribution accurately reflects collaborative contributions to complex technical models. If affiliation errors occur, it introduces noise into the established structure of scholarly authority, potentially complicating the trust placed in the methodologies described. The pattern suggests that attention must remain focused on the integrity of the provenance—who is credited where—as this forms the foundation upon which all subsequent claims of expertise rest.
BRIDGE QUESTIONS: How does the specific structure of author affiliations correlate with the funding sources or institutional mandates driving the research? What are the long-term implications for authorship and accountability when organizational structures shift post-publication? What mechanisms exist to ensure that institutional accuracy is automatically verified before publication, minimizing noise introduced by simple administrative errors?