Bibliographic record
Abstract
Retrotransposons constitute more than 40 percent of the human genome with L1, Alu, SVA, and HERVs known to remain active in transposition. Retrotransposition contribute to genetic diversity in the form of retrotransposon insertion polymorphism (RIP) that is defined as the presence or absence of a retrotransposon insertion among human populations at a specific genomic location. So far close to 5000 cases of RIPs have been identified with more than 50 cases associated with disease. A large number of new RIPs are being and to be identified from newly available personal genomes data, making RIPs an important source of genetic variations/mutations that deserve proper documentation. In this review, we discuss the special characteristics of RIPs and the challenges in their compiling and annotating, and we examine the current status of database documentation of RIPs and describe in details the design, data schema, and utilities of dbRIP, which is currently the only database dedicated to the documentation of retrotransposon insertion polymorphism. Some future perspectives and outstanding issues associated with documentation of RIPs are also presented.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".