Implementing a biogeographic ancestry inference service for forensic casework
Bibliographic record
Abstract
The Centre of Forensic Sciences has validated the Precision ID Ancestry Panel on the Ion S5™ Massively Parallel Sequencing instrument for use in forensic casework. The focus of this paper is the development of reporting guidelines for implementation of the biogeographic ancestry inference service based on the Admixture Prediction results produced using the Torrent Suite™ Software (Thermo Fisher Scientific). The Admixture Prediction algorithm estimates the genetic ancestry of a sample using seven root populations (Europe, East Asia, Oceania, America, Africa, South Asia, and Southwest Asia). For individuals that declared a single ancestry, there was a high correlation between the declared ancestry and the ancestry predicted by the algorithm. However, some individuals with declared ancestries of Southern Europe, Southwest Asia, South Asia and Horn of Africa had Admixture Predictions that were composed of two or more root populations at 20% or greater. For individuals with known admixed ancestry, the major component of their declaration was included in their results in all but one case. Based on these results, reporting guidelines were developed and subsequently evaluated using the Admixture Predictions of additional samples. This paper discusses the development and evaluation of these reporting guidelines, along with an implementation plan for forensic casework.
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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 | 0.148 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".