Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids
Bibliographic record
Abstract
Subgenome asymmetry (SA) has routinely been attributed to different responses between the subgenomes of a polyploid to various stimuli during evolution. Here, we compared subgenome differences in gene ratio and relative diversity between artificial and natural genotypes of several allopolyploid species. Surprisingly, consistent differences were not detected between these two types of polyploid genotypes, although they differ in times exposed to evolutionary selection. The estimated ratio of shared genes between a subgenome and its diploid donor was invariably higher for the artificial allopolyploid genotypes than those for the natural genotypes, which is expected as it is now well-known that many genes in a species are not shared among all individuals. As the exact diploid parent for a given subgenome is unknown, the estimated ratios of shared genes for the natural genotypes would also include difference among individual genotypes of the diploid donor species. Further, we detected the presence of SA in genotypes before the completion of the polyploidization events as well as in those which were not formed via polyploidization. These results indicate that SA may, to a large degree, reflect differences between its diploid donors or that changes occurred during polyploid evolution are defined by their donor genomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".