Molecular hyperdiversity and evolution in very large populations
Bibliographic record
Abstract
The genomic density of sequence polymorphisms critically affects the sensitivity of inferences about ongoing sequence evolution, function and demographic history. Most animal and plant genomes have relatively low densities of polymorphisms, but some species are hyperdiverse with neutral nucleotide heterozygosity exceeding 5%. Eukaryotes with extremely large populations, mimicking bacterial and viral populations, present novel opportunities for studying molecular evolution in sexually reproducing taxa with complex development. In particular, hyperdiverse species can help answer controversial questions about the evolution of genome complexity, the limits of natural selection, modes of adaptation and subtleties of the mutation process. However, such systems have some inherent complications and here we identify topics in need of theoretical developments. Close relatives of the model organisms Caenorhabditis elegans and Drosophila melanogaster provide known examples of hyperdiverse eukaryotes, encouraging functional dissection of resulting molecular evolutionary patterns. We recommend how best to exploit hyperdiverse populations for analysis, for example, in quantifying the impact of noncrossover recombination in genomes and for determining the identity and micro-evolutionary selective pressures on noncoding regulatory elements.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".