Research Status and Hotspots Analysis of Cranberry Food Based on Bibliometric
Bibliographic record
Abstract
Based on the bibliometric method, relevant research literature in the field of cranberry food included in the CNKI database from 2013 to 2023 was searched and further analyzed. A total of 444 Chinese and English literatures were obtained through screening, and CiteSpace software was used to analyze the current research status and hotspots in this field from the perspectives of annual publication volume, issuing journals, countries, authors and their institutions, as well as co-occurrences, clustering, and emergent words of keywords. The results showed that the overall trend of the number of publications from 2013 to 2023 was stable, with an average of about 40 publications per year. The journal with the highest number of publications was Food Chemistry (25), which accounted for 6% of the total literature, followed by Food & Function (22) and Journal of Agricultural and Food Chemistry (20). The top 3 countries in terms of the number of articles published were the United States, Poland, and Canada. The author with the most postings was Christina Khoo (17), a researcher from Ocean Spray, USA. The keyword analysis in the CNKI database showed that the keywords with the highest frequency were cranberry, proanthocyanidins, cranberry juice, bioactive components, urinary tract infection, antioxidant properties, intestinal microbiota and processing, health functions, and so on. The hotspots of the literature research in the field of cranberry food were mostly focused on the study of cranberry composition and function, and the exploration of the application of new processed products such as cranberry juice in the food industry. By comprehensively analyzing the literature in the CNKI database, this article can point out the direction for researchers in China cranberry food industry to engage in related research, and provide data references and help in predicting the future development trend of the industry.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.075 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 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".