Bibliographic record
Abstract
Gene duplication provides a profusion of raw material for evolutionary innovation (Lynch and Conery, 2000).While most duplicates rapidly become unrecognizable some, e.g., those that are immediately useful or those that after a period of relaxed selection gain unique roles, are retained and thereby expand a genomes proteincoding repertoire.Ohno (1970) famously remarked that without gene duplication, the creation of metazoans, vertebrates, and mammals from unicellular organisms would have been impossible.Such big leaps in evolution, he argued, required the creation of new gene loci with previously nonexistent functions (Taylor and Raes, 2004).Recently, another source of new genes has been recognized: Though rare, it seems clear that new genes can emerge from formerly non-coding DNA, the 'de novo' protein coding genes (Zhao et al., 2014, and references therein).In 2009 Knowles and McLysaght reported the discovery of three human genes derived from non-coding DNA.They provided evidence that these genes, CLUU1, C22orf45, and DNAH10OS, were transcribed and translated, they identified orthologous non-coding DNA in chimpanzee (Pan troglodytes) and macaque (Macaca mulatta), and for each gene they located the critical 'enabler' mutations that extended the open reading frames (ORFs) allowing the production of a protein.These genes had no BLASTp hits in any other genome and were considered to be novel human genes, possibly responsible for human-specific traits.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".