基于CiteSpace的亚麻籽研究热点 及趋势的可视化分析Visual analysis of research hotspots and trends of flaxseed based on CiteSpace WU Faliang1,2, PEI Yanan1,2, FAN Zhiguo1,2, WANG Qinsheng3, SUN Xiaodong4, LI Xingke1,2
Bibliographic record
Abstract
为了促进亚麻籽的开发和利用，基于2013—2023年Web of Science核心合集数据源，采用CiteSpace软件的文献计量分析方法，对亚麻籽相关论文发表数量，主要发文国家、机构和作者，关键词共现，文献共被引进行了可视化分析，基于此归纳出亚麻籽的研究热点，并提出研究趋势。结果表明：2013—2023年，亚麻籽领域的年发表论文数量和被引频次总体均呈现先上升后下降的趋势；中国、印度和加拿大等国家发表论文数量最多；埃及知识库、加拿大农业与农业食品部和中国农业科学院等是主要的研究机构；邓乾春是该领域发表论文数量最多的作者，其次是Boaventura和Reaney；大多数研究集中在亚麻籽油、脂肪酸、亚麻籽油的质量和性能等方面。近年来，亚麻籽活性成分的研究以及功能性食品、药物、生物材料等开发成为新的热点。未来几年可以从亚麻籽在医药与保健品、化工和化妆品等领域中的应用，亚麻籽的安全性研究，亚麻籽产品质量稳定方面的研究，亚麻籽种质资源和种植的研究等进行深入探索。In order to promote the development and use of flaxseed, based on the data source of Web of Science Core Collection during 2013-2023, the bibliometric analysis method of CiteSpace software was used to analyze the number of publications, major issuing countries, institutions and authors, keyword co-occurrence, and literature co-citation of papers related to flaxseed were visualized and analyzed, based on this, the research hotspots of flaxseed were summarized, and the trends were analyzed. The results showed that from 2013 to 2023, the annual number of papers and citation frequency in the field of flaxseed showed an increasing and then decreasing trend overall.China, India, and Canada were the countries with the most published papers. Egyptian Knowledge Bank, Agriculture & Agric Food Canada, and Chinese Academy of Agricultural Sciences were the major research institutions. Deng Qianchun was the author with the most published papers in this field, followed by Boaventura and Reaney. Most of the studies focused on flaxseed oil, fatty acids, quality and properties of flaxseed oil. In recent years, the study of active components of flaxseed and the development of functional foods, drugs and biomaterials have become new hotspots.The research in the next few years can be explored in depth from the application of flaxseed in the fields of medicine, health product, chemical industry, cosmetics and other fields, the safety of flaxseed, the stabilization of the quality of the product of flaxseed, the germplasm resources and cultivation of flaxseed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".