Statistical support for identification using epigenetic traits of the human skeleton
Bibliographic record
Abstract
Radiologic comparison is a potentially reliable means of identification in forensic contexts. Most radiologic comparisons are subjective and involve a qualitative visual comparison of the degree of similarity between antemortem and postmortem images, which is insufficient for quantitatively assessing the evidentiary value of an identification. Rather than simply concluding that antemortem and postmortem radiologic comparisons appear the same in the opinion of the examiner, results should be expressed quantitatively. This bolsters conclusions by providing statistical support for the probability of correct identification. Epigenetic trait variation is assessed by a forensic anthropologist during the examination of unknown human skeletal remains and may be useful in establishing positive identification, and/or in providing investigative direction. A key factor in this regard is the frequency of the trait(s) being compared in a given population. The present study utilizes epigenetic trait data from a preceding publication to demonstrate a method of statistically quantifiable positive identification based on epigenetic trait frequencies, ultimately demonstrating the utility of this method in practice. Utilizing a case study approach, the present authors demonstrate the benefits of a combined likelihood approach and propose standards for the presentation of likelihood ratios and verbal equivalent statements, to promote consistency in the reporting of results. • Epigenetic traits may be used for positive identification in forensic casework • Traits may support putative identification at the scene, contributing to timely case resolution • A combined likelihood ratio helps to assess and communicate the strength of an identification • Standards are proposed for presentation of likelihood ratios and related statements
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".