Current trends in forest science research using microsatellite markers in Korean national journals
Bibliographic record
Abstract
Microsatellites, which are sequences of repetitive short nucleotides, are abundant in the genome and have relatively many alleles at a locus. Hence, microsatellite markers are used in various research areas such as medicine, agriculture, and biology. Thanks to recent advanced techniques and databases associated with microsatellite marker development, foreign research relying on microsatellite markers is increasing in various study areas. In this study, by analyzing microsatellites-related articles published during 2000-2014 from eight Korean national journals representing zoology, botany, genetics, ecology and environmental science, breeding science, and forest science ('Animal Cells and Systems', 'Journal of Plant Biology', 'Genes and Genomics', 'Korean Society of Environment and Ecology', 'Korean Journal of Breeding Science', 'Journal of Agricultural Science, Chungnam National University', 'Journal of Korean Forest Society' and 'Forest Science and Technology'), we found that the number of articles and diversity of study subjects and objects have increased considerably. However, there are fewer applications of microsatellites in the national forest science area. During 2000-2014 in 'Journal of Korean Forest Society', the percentage of articles dealing with microsatellite markers was found to be the lowest with 4.2% among articles focusing on PCR-based markers including RAPD, AFLP, and ISSR. However, in 'Canadian Journal of Forest Research' and 'Forest Ecology and Management', microsatellite marker articles were represented at their highest with 69.2% and 76.2%, respectively. Given the advantages of microsatellite markers, the publication of research papers using microsatellites should be increased in Korean forest science journals to the level of studies published in prominent international journals.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".