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Learning to identify relevant studies for systematic reviews using random forest and external information., , , , and . Mach. Learn., 102 (3): 465-482 (2016)SeerSuite: developing a scalable and reliable application framework for building digital libraries by crawling the web, , , , , and . Proceedings of the 2010 USENIX conference on Web application development, page 14--14. Berkeley, CA, USA, USENIX Association, (2010)AckSeer: a repository and search engine for automatically extracted acknowledgments from digital libraries., , and . JCDL, page 185-194. ACM, (2012)Quantifying Adaptability in Pre-trained Language Models with 500 Tasks., , , , , and . NAACL-HLT, page 4696-4715. Association for Computational Linguistics, (2022)The impact of user corrections on a crawl-based digital library: A CiteSeerX perspective., , , and . CollaborateCom, page 171-176. ICST / IEEE, (2014)UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning., , , , , , , and . ACL (1), page 6253-6264. Association for Computational Linguistics, (2022)To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks., , and . ACL, page 2209-2213. Association for Computational Linguistics, (2020)The feasibility of investing in manual correction of metadata for a large-scale digital library., , and . JCDL, page 225-228. IEEE Computer Society, (2014)XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models., , , , , , , and . EMNLP, page 13142-13152. Association for Computational Linguistics, (2023)Specialized Research Datasets in the CiteSeerx Digital Library., , , , , , , , and . D Lib Mag., (2012)