Bibliographic record
Abstract
Abstract Pseudogenes are those regions in a genome that have sequence similarity to functional genes but have decayed and have no obvious functions. It is estimated that the human genome contains more than 10 000 easily recognisable pseudogenes and many more fragmented sequences, that arose mainly through one of the following three mechanisms: duplication, retrotranposition and spontaneous loss of function. The majority of the human retrotransposed (i.e. processed) pseudogenes are primate specific, arising from a burst of retrotransposition activities approximately 45 Ma. Although most of the human pseudogenes are most likely too degenerated to perform a biological function, ∼20% of them exhibit evidence of transcriptional activity based on data from multiple genomic studies. Furthermore, a handful of pseudogene transcripts have been demonstrated experimentally to gain novel functions as noncoding ribonucleic acids (RNAs) , indicating that pseudogenes could be a reservoir for evolution innovation. Key Concepts: Pseudogenes are prevalent in the human genome and other mammalian genomes. Most human pseudogenes are from past retrotranspositions occurring before the split of primate from other lineages. Pseudogenes are a good source of DNA sequences for studying genome evolution. Most human pseudogenes are most likely ‘dead’ but many of them can be transcribed. Some human pseudogenes have adopted functions as noncoding RNAs.
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) | 0.000 | 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".