Exploring new short tandem repeat markers for <scp>DNA</scp> mixture deconvolution
Bibliographic record
Abstract
Abstract Relying on their polymorphic nature, short tandem repeats (STRs) have been extensively studied and routinely utilized as human identity markers in forensic genetics. However, even the most comprehensive STR multiplexes in use today are limited in their ability to determine the number and appropriation of component contributor alleles in DNA mixtures. The difficulty in parsing out individuals in DNA mixtures is a consequence of overlapping length‐based alleles genotyped using the polymerase chain reaction (PCR) coupled with capillary electrophoresis (CE). Many challenges exist in the resolution of minor alleles (i.e., the alleles originating from a minor contributor) from stutter and stochastic effects (e.g., inherent heterozygote peak imbalance, undetected alleles [drop out]) in a given DNA profile, in addition to the complex statistical models and algorithms necessary to render DNA mixtures interpretable from a forensic casework standpoint. Therefore, we can either adapt to complex interpretation methods, or pursue new bench science approaches to DNA mixture deconvolution. One promising area of research includes the incorporation of additional highly polymorphic STR loci to compliment current marker multiplexes and offer great potential to improve the forensic genetic analysis of DNA mixtures. This article is categorized under: Forensic Biology > Forensic DNA Technologies Forensic Biology > Interpretation of Biological Evidence Forensic Biology > Ethical and Social Implications
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".