Bibliographic record
Abstract
Genetic mosaics provide information about cellular lineages that is otherwise difficult to obtain, especially in humans. De novo mutations act as cell markers, allowing the tracing of developmental trajectories of all descendants of the cell in which the new mutation arises. De novo mutations may arise at any time during development but are relatively rare. They have usually been observed through medical ascertainment, when the mutation causes unusual clinical signs or symptoms. Mutational events can include aneuploidies, large chromosomal rearrangements, copy number variants, or point mutations. In this review we focus primarily on the analysis of point mutations and their utility in addressing questions of germ line versus somatic lineages. Genetic mosaics demonstrate that the germ line and soma diverge early in development, since there are many examples of combined somatic and germ line mosaicism for de novo mutations. The occurrence of simultaneous mosaicism in both the germ line and soma also shows that the germ line is not strictly clonal but arises from at least two, and possibly multiple, cells in the embryo with different ancestries. Whole genome or exome DNA sequencing technologies promise to expand the range of studies of genetic mosaics, as de novo mutations can now be identified through sequencing alone in the absence of a medical ascertainment. These technologies have been used to study mutation patterns in nuclear families and in monozygotic twins, and in animal model developmental studies, but not yet for extensive cell lineage studies in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".