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dc.contributor.authorSyed, Shaheen
dc.contributor.authorWeber, Charlotte Teresa
dc.date.accessioned2019-01-15T14:42:32Z
dc.date.available2019-01-15T14:42:32Z
dc.date.issued2018-01-16
dc.description.abstractModeling has become the most commonly used method in fisheries science, with numerous types of models and approaches available today. The large variety of models and the overwhelming amount of scientific literature published yearly can make it difficult to effectively access and use the output of fisheries modeling publications. In particular, the underlying topic of an article cannot always be detected using keyword searches. As a consequence, identifying the developments and trends within fisheries modeling research can be challenging and time-consuming. This paper utilizes a machine learning algorithm to uncover hidden topics and subtopics from peer-reviewed fisheries modeling publications and identifies temporal trends using 22,236 full-text articles extracted from 13 top-tier fisheries journals from 1990 to 2016. Two modeling topics were discovered: estimation models (a topic that contains the idea of catch, effort, and abundance estimation) and stock assessment models (a topic on the assessment of the current state of a fishery and future projections of fish stock responses and management effects). The underlying modeling subtopics show a change in the research focus of modeling publications over the last 26 years.en_US
dc.descriptionSource at <a href=https://doi.org/10.1080/23308249.2017.1416331> https://doi.org/10.1080/23308249.2017.1416331</a>.en_US
dc.identifier.citationSyed, S. & Weber, C.T. (2018). Using Machine Learning to Uncover Latent Research Topics in Fishery Models. <i>Reviews in Fisheries Science & Aquaculture</i>, 26(3), 319-336. https://doi.org/10.1080/23308249.2017.1416331en_US
dc.identifier.cristinIDFRIDAID 1583163
dc.identifier.doi10.1080/23308249.2017.1416331
dc.identifier.issn2330-8249
dc.identifier.issn2330-8257
dc.identifier.urihttps://hdl.handle.net/10037/14452
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofWeber, C.T. (2019). Towards a framework to guide and facilitate interdisciplinary social-ecological system research in practice. (Doctoral thesis). <a href=https://hdl.handle.net/10037/15238>https://hdl.handle.net/10037/15238</a>.
dc.relation.journalReviews in Fisheries Science & Aquaculture
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020-EU.1.3.1./642080/EU/Social Science Aspects of Fisheries for the 21st Century/SAF21/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920en_US
dc.subjectVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920en_US
dc.subjectTopic modelsen_US
dc.subjectlatent Dirichlet allocationen_US
dc.subjectfisheries scienceen_US
dc.subjectfisheries modelsen_US
dc.subjectresearch trendsen_US
dc.titleUsing Machine Learning to Uncover Latent Research Topics in Fishery Modelsen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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