title: Science communication. Comment on "Quantifying long-term scientific impact".
abstract: Wang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.
authors.affiliation: Center for R&D Monitoring (ECOOM) and Department of Managerial Economics, Strategy and Innovation, University of Leuven, Waaistraat 6, Bus 3536, 3000 Leuven, Belgium. Institute for Research Information and Quality Assurance (iFQ), Schuetzenstrasse 6a, 10117 Berlin, Germany. jian.wang@kuleuven.be.
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
authors.affiliation: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Drive Northwest, Atlanta, GA 30332-0205, USA.
authors.forename: Yajun
authors.initials: Y
authors.lastname: Mei
authors.affiliation: School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA 30332-0345, USA.
authors.forename: Diana
authors.initials: D
authors.lastname: Hicks
mesh_terms: D055811:Journal Impact Factor; D008962:Models, Theoretical