Significant positive correlation between the recombination rate and GC content in the human pseudoautosomal region
Bibliographic record
Abstract
This paper establishes that recombination drives the evolution of GC content in a significant way. Because the human P-arm pseudoautosomal region (PAR1) has been shown to have a high recombination rate, at least 20-fold more frequent than the genomic average of approximately 1 cM/Mb, this region provides an ideal system to study the role of recombination in the evolution of base composition. Nine non-coding regions of PAR1 are analyzed in this study. We have observed a highly significant positive correlation between the recombination rate and GC content (rho = 0.837, p < or = 0.005). Five regions that lie in the distal part of PAR1 are shown to be significantly higher than genomic average divergence. By comparing the intra- and inter-specific AT->GC -GC->AT ratios, we have detected no fixation bias toward GC alleles except for L254915, which has excessive AT-->GC changes in the human lineage. Thus, we conclude that the high GC content of the PAR1 genes better fits the biased gene conversion (BGC) model.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".