Special Issue: Repetitive DNA Sequences
Bibliographic record
Abstract
Repetitive DNAs are ubiquitous in eukaryotic genomes and, in many species, comprise the bulk of the genome. Repeats include transposable elements that can self-mobilize and disperse around the genome and tandemly-repeated satellite DNAs that increase in copy number due to replication slippage and unequal crossing over. Despite their abundance, repetitive DNAs are often ignored in genomic studies due to technical challenges in identifying, assembling, and quantifying them. New technologies and methods are now allowing unprecedented power to analyze repetitive DNAs across diverse taxa. Repetitive DNAs are of particular interest because they can represent distinct modes of genome evolution. Some repetitive DNAs form essential genome structures, such as telomeres and centromeres, that are required for proper chromosome maintenance and segregation, while others form piRNA clusters that regulate transposable elements; thus, these elements are expected to evolve under purifying selection. In contrast, other repeats evolve selfishly and cause genetic conflicts with their host species that drive adaptive evolution of host defense systems. However, the majority of repeats likely accumulate in eukaryotes in the absence of selection due to mechanisms of transposition and unequal crossing over. However, even these "neutral" repeats may indirectly influence genome evolution as they reach high abundance. In this Special Issue, the contributing authors explore these questions from a range of perspectives.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".