Bibliographic record
Abstract
A retrovirus inserts its genome into the DNA of a cell, occasionally a germ-line cell that gives rise to descendants of the host organism: it is then called an endogenous retrovirus (ERV). The human genome contains relics from many kinds of ancient ERV. Some relics contributed new genes and regulatory elements. This study finds further kinds of ancient ERV, in the thoroughly-studied human genome version hg38: ERV-Hako, ERV-Saru, ERV-Hou, ERV-Han, and ERV-Goku. It also finds many relics of ERV-V, previously known from just two copies on chromosome 19 with placental genes. It finds a type of ERV flanked by MER41E long terminal repeats (LTRs), with surprisingly little similarity to the known MER41 ERV. ERV-Hako has subtypes that contain sequence from host genes SUSD6 and SPHKAP: the SUSD6 variant was transferred between catarrhine and platyrrhine primates. A retrovirus uses tRNA to prime reverse transcription: Hako is the only human ERV relic that used tRNA-Trp (tryptophan, symbol W), and HERV-W is misnamed because it used tRNA-Arg, based on the Genomic tRNA Database. One ERV-Saru LTR is the previously-described enhancer of AIM2 in innate immunity. This study contributes to understanding primate ERV history, but also shows that related ERVs can have drastic differences, challenging the goal of clearly annotating all ERV relics in genomes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".