Properties of Samples With Segregating Polymerase Chain Reaction (PCR) Dropout Mutations Within a Species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In polymerase chain reaction (PCR)-based DNA sequencing studies, there is the possibility that mutations at the binding sites of primers result in no primer binding and therefore no amplification. In this article, we call such mutations PCR dropouts and present a coalescent-based theory of the distribution of segregating PCR dropout mutations within a species. We show that dropout mutations typically occur along branch sections that are at or near the base of a coalescent tree, if at all. Given that a dropout mutation occurs along a branch section near the base of a tree, there is a good chance that it causes the alleles of a large fraction of a species to go unamplified, which distorts the tree shape. Expected coalescence times and distributions of pairwise sequence differences in the presence of PCR dropout mutations are derived under the assumptions of both neutrality and background selection. These expectations differ from when PCR dropout mutations are absent and may form the basis of inferential approaches to detect the presence of dropout mutations, as well as the development of unbiased estimators of statistics associated with population-level genetic variation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it