Total allele count distribution (TAC curves) improves number of contributor estimation for complex DNA mixtures
Bibliographic record
Abstract
As the forensic community is transitioning to probabilistic genotyping and the use of likelihood ratios to assign probative weight to DNA mixtures, the assessment of the number of contributors (NOC) needs to be more robust for mixture interpretation. However, NOC assessment can be challenging for low-template and/or high order mixtures. Here, we present a quick and easy-to-use tool to help with NOC estimation: total allele count curves (TAC curves). TAC curves for two to seven contributors were generated using sets of 20,000 in silico mixtures, for five populations (African American, Caucasian, Asian, Apache and Native Alaska) and for commonly used commercial STR kits (GlobalFilerTM, PowerPlex® Fusion, PowerPlex® ESX 17 and IdentifilerTM). To assess the performance of TAC curves, the NOC was evaluated for 80 mixtures, with and without use of the curves. Results show that TAC curves allow for a better NOC assessment as correct evaluations rose from 10% when using maximal allele count (MAC) to 65% when also using TAC for four to six contributor mixtures. Supplemental data for this article is available online at http://dx.doi.org/10.1080/00085030.2022.2028359 .
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".