Genetic Diversity in Southeast European \nSoybean Germplasm Revealed by SSR \nMarkers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breeding material and registered soybean cultivars in Southeast European countries \nare strongly linked to Western breeding programs, primarily in the USA and Canada. \nTh ere is little reliable information regarding the source of germplasm introduction, \nits pedigree and breeding schemes applied. Consequently, use of these genotypes in \nmaking crosses to develop further breeding cycles can result in an insuffi cient level of \ngenetic variability. \nTh e objective of this study was to assess genetic diversity and relationships of \n23 soybean genotypes representing several independent breeding sources from \nSoutheastern Europe and fi ve plant introductions from Western Europe and Canada \nusing 20 SSR markers. \nIn total 80 alleles were detected among 28 genotypes with an average of four alleles \nper locus and an average marker diversity of 0.585. Allele frequency distribution \nwas characterised with a high proportion of alleles at very low frequencies with 11 \n% of unique alleles. Cluster analysis clearly separated all genotypes from each other \nassigning them into three major clusters, which largely corresponded to their origin. \nResults of clustering were mainly in accordance with the known pedigrees. \n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it