Bibliometric Analysis of Research History, Hotspots, and Emerging Trends on Flax with CiteSpace (2000-2022)
Bibliographic record
Abstract
With the growing health and environmental consciousness, flax (Linum usitatissimum L.) has attracted more attention owing to its great potential in the food, health care, and material industry. For such an important crop, it is crucial to understand its development history, current status, and hotspots and finally find the future directions of flax research. This paper mainly analyzed the published articles (collected from the Web of Science) related to flax from 2000–2022 and the cited references by these articles using the software of CiteSpace. Results showed that the number of studies on flax kept increasing and increased rapidly from 2010 to 2022. Canada and France are the leading countries in flax research with more than 970 articles published during the period. By analyzing the high-frequency keywords, five important research areas were found: (1) flax fiber quality and its application in composites, (2) chemical composition and products of flaxseed, (3) tolerance of flax to stress and genetics, (4) cellulose and lignin, (5) fiber-reinforced composites and flax fabric. With the strongest citation bursts, bio-composite with flax straw has become the hottest research area for flax. In the future, efforts should still be made to the simplified and efficient production of flax owing to the higher labor cost, and more attention should be paid to healthier flaxseed food and flax-based environmentally friendly biomaterials. Finally, decreasing the cost of cultivation and pre-processing and developing end products with higher values would greatly promote the development of the whole flax industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.201 | 0.271 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".