NIST interlaboratory studies involving DNA mixtures (MIX13): A modern analysis
Bibliographic record
Abstract
MIX13 was an interlaboratory exercise directed by NIST in 2013. The goal of the exercise was to evaluate the general state of interpretation methods in use at the time across the forensic community within the US and Canada and to measure the consistency in mixture interpretation. The findings were that there was a large variation in analysts’ interpretations between and within laboratories. Within this work, we sought to evaluate the same mock mixture cases analyzed in MIX13 but with a more current view of the state-of-the-science. Each of the five cases were analyzed using the Identifiler™ multiplex and interpreted with the combined probability of inclusion, CPI, and four different modern probabilistic genotyping systems. Cases 1–4 can be interpreted without difficulty by any of the four PG systems examined. Cases 1 and 4 could also be interpreted successfully with the CPI by assuming two donors. Cases 2 and 3 cannot be interpreted successfully with the CPI because of potential of allele dropout. Case 3 demonstrated the need to consider relevant background information before interpretation of the profile. This case does not show that there is some barrier to interpretation caused by relatedness beyond the increased allelic overlap that can occur. Had this profile been of better template it might have been interpreted using the CPI despite the (potential) relatedness of contributors. Case 5 suffers from over-engineering. It is unclear whether reference 5C, a non-donor, can be excluded by manual methods. Inclusion of reference 5C should be termed an adventitious match not a false inclusion. Beyond this statement this case does not contribute to the interlaboratory study of analyst/laboratory interpretation method performance, instead, it explores the limits of DNA analysis. Taken collectively the analysis of these five cases demonstrates the benefits of changing from CPI to a PG system.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".