Nutritional composition and health benefits of peas—a bibliometric research
Bibliographic record
Abstract
L.) is a nutritious legume with health benefits, gaining attention as a functional food. Bibliometric studies use quantitative methods to assess research trends, gaps, and future directions. The main objective of this study was to provide a comprehensive overview of the fragmented literature on the nutritional profiles and health benefits of peas using a bibliometric approach. The analysis examined publications from 2013 to 2023, revealing trends in publication volume, author productivity, and international collaboration. Publications peaked in 2015, focusing on topics such as dietary fibers, carotenoids, phenolic compounds, and antinutrients affecting mineral bioavailability. Over the decade, the annual growth rate was 3.25%. The University of Saskatchewan produced the most influential research, with Warkentin TD as the most productive author. Canada and Poland had the highest number of publications, with the USA, China, and India following. Six major international co-authorship networks were identified, highlighting significant collaborations between countries. Key research themes included antioxidants, protein, fiber, and phytate in peas. This study provides a strong foundation for future integrated research, helping to better understand the potential of peas as a functional food and guiding more targeted studies to address current knowledge gaps across various disciplines.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.051 | 0.064 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".