Bibliographic record
Abstract
Transposable elements exhibit a base composition that is often different from the genomic average and from hosts' genes. The most common compositional bias is towards Adenosine and Thymine, although this bias is not universal, and elements with drastically different base composition can coexist within the same genome. The AT-richness of transposable elements is apparently maladaptive because it results in poor transcription and sub-optimal translation of proteins encoded by the elements. The cause(s) of this unusual base composition remain unclear and have yet to be investigated. Here, I review what is known about the nucleotide content of transposable elements and how this content can affect the genome of their host as well as their own replication. The compositional bias of transposable elements could result from several non-exclusive processes including horizontal transfer, mutational bias, and selection. It appears that mutation alone cannot explain the high AT-content of transposons and that selection plays a major role in the evolution of the compositional bias. The reason why selection would favor a maladaptive nucleotide content remains however unexplained and is an area of investigation that clearly deserves attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".