A ROLE FOR NONADAPTIVE PROCESSES IN PLANT GENOME SIZE EVOLUTION?
Bibliographic record
Abstract
Genome sizes vary widely among species, but comprehensive explanations for the emergence of this variation have not been validated. Lynch and Conery (2003) hypothesized that genome expansion is maladaptive, and that lineages with small effective population size (N(e)) evolve larger genomes than those with large N(e) as a consequence of the lowered efficacy of natural selection in small populations. In addition, mating systems likely affect genome size evolution via effects on both N(e) and the spread of transposable elements (TEs). We present a comparative analysis of the effects of N(e) and mating system on genome size evolution in seed plants. The dataset includes 205 species with monoploid genome size estimates (corrected for recent polyploidy) ranging from 2Cx = 0.3 to 65.9 pg. The raw data exhibited a strong positive relationship between outcrossing and genome size, a negative relationship between N(e) and genome size, but no detectable N(e)x outcrossing interaction. In contrast, phylogenetically independent contrast analyses found only a weak relationship between outcrossing and genome size and no relationship between N(e) and genome size. Thus, seed plants do not support the Lynch and Conery mechanism of genome size evolution. Further work is needed to disentangle contrasting effects of mating systems on the efficacy of selection and TE transmission.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".