Measuring the Evolutionary Distances between Brassicaceae Species
Bibliographic record
Abstract
Evolutionary relationships can help us understand the history and evolution of a species. Evolutionary relationships are often discovered or confirmed by molecular phylogeny, which allows us to compare species by their genomes. About 9-15 million years ago, the Brassica genus, which includes canola, broccoli, mustards, and other plants, experienced a whole genome triplication event. This event not only increased the size of the genome, but also the number of duplicated genes within it. We used the Genome REarrangements with Duplications (GREDU) software package to find the double-cut-and-join edit distances between five Brassicaceae species, three of which were from the Brassica genus. GREDU rearranges genomes to find an approximation of the smallest number of rearrangements that must be made to transform one genome into the other. The smaller the number, the closer any two species are predicted to be. GREDU notably supports the comparison of genomes with duplicate genes included in the calculation, which is important when working with Brassica species. We were able to reconstruct the widely accepted phylogenetic tree of the species studied, however we discovered that the GREDU tool may not be best when comparing Brassica species due to the high number of duplicate genes. Future areas of research include determining the actual edit distances between species and analyzing whether GREDU is an appropriate package for conducting comparisons when species have a high number of duplicated genes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".