The rate of chromosomal inversion fixation in plant genomes is highly variable
Bibliographic record
Abstract
Chromosomal inversions are theorized to play an important role in adaptation by preventing recombination, but testing this hypothesis requires an understanding of the rate of inversion fixation. Here, we use chromosome-level whole-genome assemblies for 32 genera of plants to ask how fast inversions accumulate and what factors affect this rate. We find that on average species accumulate 4-25 inversions per million generations, but this rate is highly variable, and we find no correlation between sequence divergence or repeat content and the number of inversions or the proportion of genome that was inverted and only a small correlation with chromosome size. We also find that inversion regions are depleted for genes and enriched for TEs compared to the genomic background. This suggests that idiosyncratic forces, like natural selection and demography, are controlling how fast inversions fix.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".