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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".