{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:30:10Z","timestamp":1759825810107},"reference-count":6,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"6193","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Science"],"published-print":{"date-parts":[[2014,7,11]]},"abstract":"<jats:p>\n            Wang\n            <jats:italic>et al<\/jats:italic>\n            . (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 \u03bc (&gt;5) and \u03bb (&gt;10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.\n          <\/jats:p>","DOI":"10.1126\/science.1248770","type":"journal-article","created":{"date-parts":[[2014,7,10]],"date-time":"2014-07-10T20:59:53Z","timestamp":1405025993000},"page":"149-149","source":"Crossref","is-referenced-by-count":3,"title":["Comment on \u201cQuantifying long-term scientific impact\u201d"],"prefix":"10.1126","volume":"345","author":[{"given":"Jian","family":"Wang","sequence":"first","affiliation":[{"name":"Center for R&amp;D Monitoring (ECOOM) and Department of Managerial Economics, Strategy and Innovation, University of Leuven, Waaistraat 6, Bus 3536, 3000 Leuven, Belgium."},{"name":"Institute for Research Information and Quality Assurance (iFQ), Schuetzenstrasse 6a, 10117 Berlin, Germany."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajun","family":"Mei","sequence":"additional","affiliation":[{"name":"H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Drive Northwest, Atlanta, GA 30332\u20130205, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Hicks","sequence":"additional","affiliation":[{"name":"School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA 30332\u20130345, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1237825"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature.2013.13881"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-012-0775-9"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"H. Shen C. Song D. Wang A.-L. Barab\u00e1si Modeling and predicting popularity dynamics via reinforced Poisson processes Twenty-Eighth AAAI Conference on Artificial Intelligence Qu\u00e9bec City Qu\u00e9bec Canada 27 to 31 July 2014; preprint available at http:\/\/arxiv.org\/abs\/1401.0778.","DOI":"10.1609\/aaai.v28i1.8739"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:SCIE.0000006881.30700.ea"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:SCIE.0000018543.82441.f1"}],"container-title":["Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/science.1248770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:04:07Z","timestamp":1704899047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/science.1248770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,7,11]]},"references-count":6,"journal-issue":{"issue":"6193","published-print":{"date-parts":[[2014,7,11]]}},"alternative-id":["10.1126\/science.1248770"],"URL":"https:\/\/doi.org\/10.1126\/science.1248770","relation":{},"ISSN":["0036-8075","1095-9203"],"issn-type":[{"value":"0036-8075","type":"print"},{"value":"1095-9203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,7,11]]}}}