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The X-Age Project to construct a Chinese aging clock
Mengmeng Jiang, Qiaoran Wang, Zikai Zheng, Jianghua Shen, Muzhao Xiong, Yandong Zheng, Xiaoyong Lu, Yusheng Cai, Yanling Fan, Lingling Geng, Qianzhao Ji, Qianqian Peng, Shuhui Sun, Yuan Yuan Wang, Zijuan Xin, Jun Yu, Haoteng Yan, Ding Ai, Yongping Bai, Yan Bi, Xiu‐Wu Bian, Pengcheng Bu, Jian‐Ping Cai, Chun-Mei Cao, Feng Cao, Zhongwei Cao, Renjie Chai, Piu Chan, Chang Chen, Chengshui Chen, Chunying Chen, Di Chen, Hou‐Zao Chen, Lin Chen, Quan Chen, Xiao Chen, Xiaochun Chen, Yu Chen, Zi‐Jiang Chen, Weimin Ci, Zhe Dai, Qiurong Ding, Birong Dong, Jiahong Dong, Jian‐Gao Fan, Shiqing Feng, Xin Feng, Yun Feng, Xiaobing Fu, Xiaolong Fu, Feng Gao, Jiangang Gao, Qiang Gao, Shaorong Gao, Yonghao Gu, Youfei Guan, Feifan Guo, Jing‐Dong J. Han, Haiping Hao, Jihui Hao, Fuchu He, Jinhan He, Ming He, Mingguang He, Qiyang He, Zhiying He, Zuhong He, Huashan Hong, Jiaxu Hong, Shengping Hou, Cheng Hu, Ping Hu, Zhibin Hu, Canhua Huang, Jun Huang, Kai Huang, Pengyu Huang, Xunming Ji, Yong Ji, Shunji Jia, Hong Jiang, Wenjian Jiang, Lingjing Jin, Zi‐Bing Jin, Shenghong Ju, Zhenyu Ju, Qing‐Peng Kong, Wei Kong, Weijia Kong, Xiangqing Kong, Guanghua Lei, Geng‐Lin Li, Ji Li, Jian Li, Mengfeng Li, Rong Li, Wei Li, Xiaojun Li, Xin Li, Liang Qingfeng, Zhen Liang, Haotian Lin, Baohua Liu, Caiyue Liu, Changsheng Liu, Feng Liu, Jianfeng Liu, Junping Liu, Ke Liu, Lin Liu, Pingsheng Liu, Qiang Liu, Qiang Liu, Tiemin Liu, Wenwen Liu, Xingguo Liu, Yajun Liu, Yong Liu, Youhua Liu, You‐Shuo Liu, Zhili Liu, Xiao Long, Yao Lu, Jian Luo, Xiang‐Hang Luo, Chunhong Ma, Shuai Ma, Xinran Ma, Jianhua Mao, Zhiyong Mao, Ng Shyh‐Chang, Guangjun Nie, Yuyu Niu, Yaojin Peng, Jun Pu, Jieyu Qi, Li Qiang, Jie Qiao, Yingying Qin, Aijuan Qu, Jing Qu, Jie Ren, Ruibao Ren, Xiong Z. Ruan, Anbing Shi, Haibo Shi, Jie Shi, Kwok-Fai So, Moshi Song, Weihong Song, Zhou Songyang, Jiacan Su, Aijun Sun, Liang Sun, Qiang Sun, Yi Eve Sun, Yu Sun, Peifu Tang, Qi-Qun Tang, Yi Tang, Jun Tao, Ling Tao, Mei Tian, Xiao‐Li Tian, Ye Tian, Xiaolin Tong, Cong‐Yi Wang, Haibo Wang, Hongmei Wang, Huating Wang, Jianan Wang, Jianwei Wang, Jiqiu Wang, Liheng Wang, Lin Wang, Miao Wang, Qiang Wang, Si Wang, Sijia Wang, Songlin Wang, Wengong Wang, Xiaoming Wang, Xiaoning Wang, Yan Wang, Yan‐Jiang Wang, Yuan Wang, Zhenning Wang, Xiawei Wei, Jianping Weng, Haitao Wu, Jihong Wu, Xiaohuan Xia, Yang Xia, Andy Peng Xiang, Guozhi Xiao, Junjie Xiao, Yichuan Xiao, Zhi‐Xiong Jim Xiao, Zhengwei Xie, Wei Xiong, Aimin Xu, Xu Hua, Lingyan Xu, Ming Xu, Liying Yan, Jiayin Yang, Jichun Yang, Liu Yang, Yun‐Gui Yang, Ze Yang, Zhenglin Yang, Hongjie Yao, Jing Ye, Chengqi Yi, Fan Yi, Honghua Yu, Yang Yu, Zhengrong Yu, Ti‐Fei Yuan, Jirong Yue, Rui Yue, Chen Zhang, Chunxiang Zhang, Cuntai Zhang, Feng Zhang, Hongbo Zhang, Hongjia Zhang, Huijie Zhang, Jie Zhang, Jingjing Zhang, Licheng Zhang, Lingqiang Zhang, Luoying Zhang, Qingjiong Zhang, Wei Zhang, Weiping J. Zhang, Xin Zhang, Xuan Zhang, Yong Zhang, Yun‐wu Zhang, Zhanjun Zhang, Zhuohua Zhang, Bing Zhao, Guoguang Zhao, Jiajun Zhao, Meng Zhao, Tongbiao Zhao, Jialin Zheng, Junke Zheng, Zhuozhao Zheng, Huixia Zhou, Lili Zhou, Xiangtian Zhou, Yongsheng Zhou, Zhongjun Zhou, Lan Zhu, Yi Zhun Zhu, Zhiming Zhu, Wenjuan Zhuang, Weiguo Zou, Weiqi Zhang, Gang Pei, Guang‐Hui Liu
Bibliographic record
VenueNature Aging · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsConstruct (python library)Population ageingDeliverablePerspective (graphical)Healthy agingScalabilityPopulationCore (optical fiber)
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 head0.014
GPT teacher head0.354
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designSimulation or modeling
Domainnot available
GenreReview
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
Citations10
Published2025
Admission routes1
Has abstractno