Bibliometric analysis of blueberry (Vaccinium corymbosum L.) research publications based on Web of Science
Bibliographic record
Abstract
Abstract This study aimed to identify and analyze the 3,872 article and review type papers of blueberry research based on Web of Science. Papers mainly written in English (3,769, 97.34%), were from 10,102 authors, 83 countries or territories, 2,033 organizations and published in 770 Journals and three book series. The top five Journals were HortScience (278, 7.18%), Journal of the American Society for Horticultural Science (272, 7.024%), Journal of Agricultural and Food Chemistry (116, 2.996%), Journal of Economic Entomology (97, 2.505%), Food Chemistry (92, 2.376%). The top five countries and regions were USA, Peoples R China, Canada, Chile and Brazil. The six most paper contributed organizations were USDA ARS, University of Florida, Michigan State University, University of Georgia, Agriculture and Agri-Food Canada, and University of Maine. The top five authors were Hancock, James F.; Rowland, Lisa J.; Ehlenfeldt, Mark K.; Lyrene, Paul M.; and Strik, Bernadine C. All keywords of the blueberry research based on Web of Science were separated into seven clusters for different research topics. This review could provide a valuable guide for designing future studies. This work is also useful for student identifying graduate schools and researchers selecting journals.
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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 | Observational | 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.036 | 0.127 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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.
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".