Research on the Method of Detecting Weak Signals in Science and Technology Based on the Literature of Potential"Sleeping Beauties”: An Example in the Field of Quantum Precision Measurement
Dong Xinyu1,2, Wang Li1,2
1. National Science Library,Chinese Academy of Sciences,Beijing 100190,China; 2. Department of Information Resource Management,School of Economics and Management, University of Chinese Academy of Sciences,Beijing 100190,China
Abstract：To capture weak but potentially high-impact signals at an early stage,this paper constructs a weak signal detection framework for science and technology based on high-potential“sleeping beauty”literature. First,typical“sleeping beauty”literature is identified using Bcp values and the three-indicator method,and positive and negative sample training sets are constructed. Subsequently,an XGBoost binary classification model is trained to predict potential“sleeping beauty”literature that may have high future impact. Next,keywords are extracted from these documents as carriers of weak signals,and their strength(DOI),diffusion degree(DOD),and growth rate are calculated for amplification and noise reduction screening. Finally,a large language model and DBSCAN clustering are used to refine themes at different granularities. An empirical study was conducted in the field of quantum precision measurement. 168 potential“sleeping beauty”literature was predicted and 464 science and technology weak signal keywords were further identified. and the study condenses five major themes:quantum amplification and one-way transmission mechanisms,quantum state stability and multi-state structures,quantum state retroaction and predictive control,etc. The feature system of this study is still incomplete,and potential contribution metrics such as the dynamic changes in the author team and the evolution of the H-index have not been validated. This framework validated the feasibility of detecting weak signals based on potential“sleeping beauty”literature,achieving a closed-loop process of signal amplification,noise reduction,and theme meaning construction,providing new insights and practical references for weak signal detection and early warning of cutting-edge technologies.
董新宇, 王丽. 基于潜在 “睡美人”文献的科技弱信号探测方法研究——以量子精密测量领域为例[J]. 中国科技论坛, 2026(5): 84-95.
Dong Xinyu, Wang Li. Research on the Method of Detecting Weak Signals in Science and Technology Based on the Literature of Potential"Sleeping Beauties”: An Example in the Field of Quantum Precision Measurement. , 2026(5): 84-95.
[1] HILTUNEN E.Weak signals in organizational futures learning[D].Helsinki:Helsinki School of Economics,2010. [2] 曾娅,沈劲松.量子科技:解锁未来发展的新质生产力[N].人民邮电,2025-04-09(1). [3] 李硕,李欣欣,张雪松,等.量子信息科技与产业发展态势及未来展望[J].中国工程科学,2025,27(1):122-132. [4] 李江,姜明利,李弱婷.引文曲线的分析框架研究:以诺贝尔奖得主的引文曲线为例[J].中国图书馆学报,2014,40(2):41-49. [5] VAN RAAN A F J.Sleeping beauties in science[J].Scientometrics,2004,59(3):467-472. [6] COSTAS R,VAN LEEUWEN T N,VAN RAAN A F J.Is scientific literature subject to a‘sell-by-date’? A general methodology to analyze the‘durability’of scientific documents[J].Journal of the American Society for Information Science and Technology,2010,61(2):329-339. [7] 张家榕,曾继城,叶鹰.3S引文现象的特征测度及学术意义:“睡美人”、“时髦女”与“天鹅”综论[J].情报学报,2017,36(12):1241-1246. [8] DU J,WU Y S.A parameter-free index for identifying under-cited sleeping beauties in science[J].Scientometrics,2018,116(2):959-971. [9] 王旭,薛宇菲,邱均平.开放科学环境下睡美人论文量化识别与影响因素研究[J].情报学报,2024,43(10):1166-1181. [10] LI X,MA X,FENG Y.Early identification of breakthrough research from sleeping beauties using machine learning[J].Journal of Informetrics,2024,18(2):101234. [11] DEY R,ROY A,CHAKRABORTY T,et al.Sleeping beauties in computer science:Characterization and early identification[J].Scientometrics,2017,113(3):1645-1663. [12] 杨宁,黄飞虎,赵爽,等.基于ts2net模型的睡美人系数预测方法研究[J].数据分析与知识发现,2024,8(11):72-82. [13] MASSÉ P.Prévision et prospective[J].Revue Prospective,1959(4):91-120. [14] ANSOFF H I.Managing strategic surprise by response to weak signals[J].California Management Review,1975,18(2):21-33. [15] COFFMAN B.Weak signal research[J].Journal of Transition Management,1997,2(1):1-10. [16] HILTUNEN E.The futures window:A medium for presenting visual weak signals to trigger employees' futures thinking in organizations[R].Helsinki:Helsinki School of Economics,2007. [17] SAUL A.Facing the future,sensing theweak signals[J].Technological Forecasting and Social Change,2006,73(8):860-865. [18] 杨波,邵婉婷.弱信号概念的各领域研究向度和理路构建[J].情报杂志,2023,42(6):1-10. [19] 沈固朝.信号分析:竞争情报研究的又一重要课题[J].图书情报工作,2009,53(20):11-14. [20] 赵小康.弱信号:识别、探测与应对[J].情报杂志,2010,29(1):159-163 [21] 董尹,刘千里,宋继伟,等.弱信号研究综述:概念、方法和工具[J].情报理论与实践,2018,41(10):147-154. [22] 徐硕,王聪聪,安欣.新兴技术弱信号扫描预判述评[J].情报杂志,2023,42(3):117-122. [23] 张慧玲,许海云,王超,等.弱信号环境下情报感知方法框架研究[J].情报理论与实践,2023,46(11):9-19. [24] VAN MERKERK R O,VAN LENTE H.Tracing emerging irreversibilities in emerging technologies:The case of nanotubes[J].Technological Forecasting & Social Change,2005,72(9):1094-1111. [25] YOON J.Detecting weak signals for long term business opportunities using text mining of web news[J].Expert Systems with Applications,2012,39(16):12543-12550. [26] THORLEUCHTER D,VAN DEN POEL D.Weak signal identification with semantic web mining[J].Expert Systems with Applications,2013,40(12):4978-4985. [27] KIM J,LEE C.Novelty focused weak signal detection in futuristic data:Assessing the rarity and paradigm unrelatedness of signals[J].Technological Forecasting and Social Change,2017,120:59-76. [28] YU T,YU G,LI P Y.Citation impact prediction for scientific papers using stepwiseregression analysis[J].Scientometrics,2014,101(2):1233-1252. [29] ONODERA N,YOSHIKANE F.Factors affecting citation rates of research articles[J].Journal of the Association for Information Science and Technology,2015,66(4):739-764. [30] 马晓迪.基于机器学习的潜在颠覆性技术识别方法研究[D].北京:北京工业大学,2023. [31] 郭斐,鄢小燕.“睡美人”文献研究综述[J].图书馆建设,2016(5):40-45. [32] RUAN X,ZHU Y,LI J.Predicting the citation counts of individual papers via a BP neural network[J].Journal of Informetrics,2020,14(3):100108. [33] BORNMANN L,LEYDESDORFF L,WANG J.How to improve the prediction based on citationimpact percentiles for years shortly after the publication date[J].Journal of Informetrics,2014,8(1):175-180. [34] CAO X,CHEN Y,LIU K R.A data analytic approach to quantifying scientific impact[J].Journal of Informetrics,2016,10(2):471-484. [35] CASTILLO C,DONATO D,GIONIS A.Estimating number of citations using author reputation[C]//CHAVEZ E,LONARDI S.Proceedings of the 14th International Symposium on String Processing and Information Retrieval.Berlin:Springer,2007. [36] 叶光辉,涂凯,韩丽,等.关键核心技术的关键衍生创新技术弱信号探测模型研究[J].情报学报,2025,44(12):1596-1609. [37] 曹海艳,王暖臣,穆歌,等.基于技术预见视角的弱信号识别研究综述[J].情报学报,2025,44(10):1342-1358. [38] WANG D,SONG C,BARABÁSI A L.Quantifying long-term scientific impact[J].Science,2013,342(6154):127-132. [39] 韩盟,陈悦,王玉奇,等.新兴技术弱信号识别:理论模型与测度方法[J].科学学研究,2024,42(11):2262-2274. [40] 田雪灿,王丽.信号处理视角下的科技前沿弱信号探测研究:以政采合同数据为例[J].数据分析与知识发现,2025,9(4):46-56. [41] ROUSSEAU P,KOTZINOS D,CAMARA D.Weak signal detection and identification in large data sets:A review of methods and applications[M].Cham:Springer,2021. [42] GHAZZAI S,GRIGORI D,RÉBAÏ R.Weak signal detection for occupational safety[C]//Proceedings of the EDBT/ICDT 2024 Workshops.New York:ACM,2024. [43] BUZZETTI L,CRISENZA G E M,MELCHIORRE P.Mechanistic studies in photocatalysis[J].Angewandte Chemie International Edition,2019,58:3730-3747. [44] 光子盒研究院.2025全球量子传感产业发展展望[R].北京:光子盒研究院,2025. [45] EUROPEAN COMMISSION.Quantum technologies flagship strategic research agenda[EB/OL].(2022-11-21)[2025-06-11].https://qt.eu/media/pdf/Quantum-Flagship_SRIA_2022.pdf.
2026, 
