Recovery of DNA from Shoes
Bibliographic record
Abstract
Shoes left at scenes of crime have historically been used as forensic links between suspect and scene, utilizing barefoot morphology. With the increased sensitivity of deoxyribonucleic acid (DNA) analysis, the question arises as to whether DNA profiles linking suspects to shoes left at scenes could replace barefoot morphology, or be used as corroborative evidence. Two studies were run in order to ascertain if the wearer of the shoe could be determined consistently. Three areas of a shoe were swabbed: the heel, the lace/tongue, and the insole. Twenty-one shoes were analysed in Study 1 and forty-three shoes were analysed in Study 2. The results of these studies indicate that DNA can be profiled in some instances, but not from every shoe. Environmental factors and the wearer's ability to slough skin cells determined if DNA was found in the shoe or not. The lace/tongue region of the shoe provides more information concerning the wearer than the other areas swabbed. This may be due to the fact that the lace/tongue region of a shoe is the main area of contact with the wearer's hand and the shoe. It should also be noted that on many of the shoes where DNA was found, the resulting profile was of mixed origin or could be attributed to multiple persons. This suggests that besides the wearer's DNA being found on the shoe, other individuals are found as well. This could be explained by secondary transfers of sloughed cellular sources of DNA. If secondary transfers are prevalent, caution should be used if DNA analysis of shoes is the only means of linking suspects to scenes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".