Multilevel Selection Theory and the Evolutionary Functions of Transposable Elements: Fig. 1.—
Bibliographic record
Abstract
One of several issues at play in the renewed debate over "junk DNA" is the organizational level at which genomic features might be seen as selected, and thus to exhibit function, as etiologically defined. The intuition frequently expressed by molecular geneticists that junk DNA is functional because it serves to "speed evolution" or as an "evolutionary repository" could be recast as a claim about selection between species (or clades) rather than within them, but this is not often done. Here, we review general arguments for the importance of selection at levels above that of organisms in evolution, and develop them further for a common genomic feature: the carriage of transposable elements (TEs). In many species, not least our own, TEs comprise a large fraction of all nuclear DNA, and whether they individually or collectively contribute to fitness--or are instead junk--is a subject of ongoing contestation. Even if TEs generally owe their origin to selfish selection at the lowest level (that of genomes), their prevalence in extant organisms and the prevalence of extant organisms bearing them must also respond to selection within species (on organismal fitness) and between species (on rates of speciation and extinction). At an even higher level, the persistence of clades may be affected (positively or negatively) by TE carriage. If indeed TEs speed evolution, it is at these higher levels of selection that such a function might best be attributed to them as a class.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".