Using Machine Learning to Uncover Latent Research Topics in Fishery Models
| Autor: | Syed, Shaheen, Weber, Charlotte Teresa, Software Systems, Sub General Intelligent Software Syst. |
|---|---|
| Přispěvatelé: | Software Systems, Sub General Intelligent Software Syst. |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Topic model Stock assessment Computer science Topic models Scientific literature latent Dirichlet allocation Management Monitoring Policy and Law Aquatic Science Machine learning computer.software_genre 01 natural sciences Latent Dirichlet allocation symbols.namesake 14. Life underwater Ecology Evolution Behavior and Systematics VDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920 Estimation Abundance estimation Fisheries science VDP::Agriculture and fishery disciplines: 900::Fisheries science: 920 business.industry 010604 marine biology & hydrobiology research trends 04 agricultural and veterinary sciences fisheries science Variety (cybernetics) Fishery 040102 fisheries symbols fisheries models 0401 agriculture forestry and fisheries Artificial intelligence business computer |
| Zdroj: | Reviews in Fisheries Science & Aquaculture Reviews in Fisheries Science & Aquaculture, 26(3), 319. Taylor and Francis Ltd. |
| ISSN: | 2330-8249 |
| Popis: | Source at https://doi.org/10.1080/23308249.2017.1416331. Modeling 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. |
| Databáze: | OpenAIRE |
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