Microsatellite Markers: The Efficient Method for the Determination of Pollen Contamination in Conifer Seed Orchards
Bibliographic record
Abstract
Seed orchards are specialized forest plantations of genetically superior candidate parents selected to produce genetically superior seeds and/or seedlings. Pollen contamination is one of the most important factors affecting the yield, adaptation, and genetic quality of seeds produced from seed orchards in forest tree breeding programs. Potential pollen from forests surrounding the seed orchard is a major concern in tree breeding because it contributes to the loss in genetic gains expected from seed orchard crops. Microsatellite markers are among the most effective markers that are frequently used for creating genetic maps of many species, determining genetic diversity, identifying genetic diseases, population genetic studies, linkage analysis, fingerprint analysis, genotyping, and parental identification. In this study, a bibliometric analysis was performed to quantitatively and qualitatively evaluate the articles published in the last 25 years on seed orchards and pollen contamination. Searching the Web of Science (WOS) with the criteria of 'forest trees' and 'seed orchards' revealed that 820 articles were published in the last 25 years. It is seen that 77 of these articles are related to pollen contamination. Canada, China, Japan, Sweden, and the USA have been the top contributors to research on pollen contamination in seed orchards of forest trees in the last 25 years, respectively. According to the data obtained, it has been shown that the genetic contamination level of forest tree species in seed orchards is generally between 5% and 90%. It has been determined that microsatellite markers are more widely used in recent years to determine the degree of pollen migration and genetic contamination. It was concluded that studies on pollen contamination were carried out in only two Turkish red pine orchards in Türkiye, which has a total of 189 seed orchards, the majority of which belong to conifers, and that similar studies should be planned in other seed orchards.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".