Probabilistic Models of Chromosome Number Evolution and the Inference of Polyploidy
Bibliographic record
Abstract
Polyploidy, the genome wide duplication of chromosome number, is a key feature in eukaryote evolution. Polyploidy exists in diverse groups including animals, fungi, and invertebrates but is especially prevalent in plants with most, if not all, plant species having descended from a polyploidization event. Polyploids often differ markedly from their diploid progenitors in morphological, physiological, and life history characteristics as well as rates of adaptation. The altered characteristics displayed by polyploids may contribute to their success in novel ecological habitats. Clearly, a better understanding of the processes underlying changes in the number of chromosomes within genomes is a key goal in our understanding of speciation and adaptation for a wide range of families and genera. Despite the fundamental role of chromosome number change in eukaryotic evolution, probabilistic models describing the evolution of chromosome number along a phylogeny have not yet been formulated. We present a series of likelihood models, each representing a different hypothesis regarding the evolution of chromosome number along a given phylogeny. These models allow us to reconstruct ancestral chromosome numbers and to estimate the expected number of polyploidization events and single chromosome changes (dysploidy) that occurred along a phylogeny. We test, using simulations, the accuracy of this approach and its dependence on the number of taxa and tree length. We then demonstrate the application of the method for the study of chromosome number evolution in 4 plant genera: Aristolochia, Carex, Passiflora, and Helianthus. Considering the depth of the available cytological and phylogenetic data, formal models of chromosome number evolution are expected to advance significantly our understanding of the importance of polyploidy and dysploidy across different taxonomic groups.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".