The frequency of aneuploidy among individual chromosomes in 6,821 human sperm chromosome complements
Bibliographic record
Abstract
The human sperm/hamster egg fusion technique has been used to analyse 6,821 human sperm chromosome complements from 98 men to determine if all chromosomes are equally likely to be involved in aneuploid events or if some chromosomes are particularly susceptible to nondisjunction. The frequency of hypohaploidy and hyperhaploidy was compared among different chromosome groups and individual chromosomes. In general, hypohaploid sperm complements were more frequent than hyperhaploid complements. The distribution of chromosome loss in the hypohaploid complements indicated that significantly fewer of the large chromosomes and significantly more of the small chromosomes were lost, suggesting that technical loss predominantly affects small chromosomes. Among the autosomes, the observed frequency of hyperhaploid sperm equalled the expected frequency (assuming an equal frequency of nondisjunction for all chromosomes) for all chromosome groups. Among individual autosomes, only chromosome 9 showed an increased frequency of hyperhaploidy. The sex chromosomes also showed a significant increase in the frequency of hyperhaploidy. These results are consistent with studies of spontaneous abortions and liveborns demonstrating that aneuploidy for the sex chromosomes is caused by paternal meiotic error more commonly than aneuploidy for the autosomes.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".