Bibliometric analysis on trends and future directions of research and development in seed orchards
Bibliographic record
Abstract
Abstract Seed orchard is one of the most important seed sources to produce improved seed crop to future plantation practices, and to transmit the current gene diversity to future generations. Seed orchard is also one of the main sub-divisions in forest science. It has many steps from selection of superior trees to harvesting of seed crop by establishment and management. Trends and future direction of seed orchards are getting importance especially to establish resistance forests against various environmental conditions due to global climate change. This study was carried out to analysis current trends of research in seed orchards for future directions in the orchards. For the purposes, 1018 published papers, hit in database of “Web of Science” by “seed orchard” keyword, between 1980 and 2022 were analyzed bibliometrical based on the most prolific contributors, references, countries and keywords. The average of citations per publication was 13.05 by 48 H-Index of the publication set. The most prolific contributors with the strongest citation burst, centrality and numbers of published papers were from Canada, Sweden South Korea, Finland and Czechia, while Canada (186 articles) USA (140), and Sweden (115) together with China, Brazil and Germany were active countries especially by citations of recent years. “Key words” of the papers were mirrors of the researches. “Matting pattern” “Swedish forestry”, “fertility variation”, “Hymenoscyphus fraxineus”, “threatened pacific sandalwood”, “outbreeding depression”, “climate change”, “management” and “growth” together with others such as “genetic improvement” and “effective size” were active study areas and key words based on results of the analysis. They were also guides in literature search, and inventory and classification of early studies, and predictor for future studies. Results of the study were discussed based on trends and future directions of research and development in seed orchards.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.113 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".