Hotspots for copy number variation in chimpanzees and humans
Bibliographic record
Abstract
Copy number variation is surprisingly common among humans and can be involved in phenotypic diversity and variable susceptibility to complex diseases, but little is known of the extent of copy number variation in nonhuman primates. We have used two array-based comparative genomic hybridization platforms to identify a total of 355 copy number variants (CNVs) in the genomes of 20 wild-born chimpanzees (Pan troglodytes) and have compared the identified chimpanzee CNVs to known human CNVs from previous studies. Many CNVs were observed in the corresponding regions in both chimpanzees and humans; especially those CNVs of higher frequency. Strikingly, these loci are enriched 20-fold for ancestral segmental duplications, which may facilitate CNV formation through nonallelic homologous recombination mechanisms. Therefore, some of these regions may be unstable "hotspots" for the genesis of copy number variation, with recurrent duplications and deletions occurring across and within species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".