General Guidelines for Categorization and Interpretation of Mixed STR DNA Profiles
Bibliographic record
Abstract
The proposed general DNA interpretation guidelines provide a comprehensive approach for categorization and interpretation of mixed forensic STR DNA profiles. Mixed DNA profiles occur when there is more than one individual contributing biological material to a sample. The guidelines represent a compilation of methods currently in use by forensic laboratories, along with a discussion of the LSD (Least Squares Deconvolution) objective mathematical approach to interpreting two-person mixtures, as well as the Combined Probability of Inclusion/Probability of Exclusion method. DNA sample features and contexts found in commonly recurring case scenarios permits sorting of profiles into one of six categories. An example profile is interpreted under various scenarios to highlight differences in interpretation dependent on case circumstances. Sorting DNA profiles into categories permits a simplified, scientifically and mathematically sound, conservative interpretation without discarding significant forensic data.
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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 | 0.033 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".