Exploring human hair degradation: A preliminary study for estimating time-since-death
Bibliographic record
Abstract
Abstract Postmortem interval (PMI) estimation is a challenging task in forensic investigations. PMI assessment frequently requires the application of the currently available methods which can lead to unsatisfactory results due to the poor accuracy of time interval estimation. To address these concerns, the present study aimed to evaluate whether there is a correlation between human hair proteolysis and PMI. Scalp hair samples of three living donors and eleven individuals exhumed from different burial types from Portuguese cemeteries were analysed by ATR-FTIR (attenuated total reflectance – Fourier-transform infrared). Four band areas and three hair degradation indices were considered in the 2000–1000 cm−1 spectral region. When analysing the entire dataset (i.e., 126 infrared spectra) – and when separating and analysing the spectroscopic data by burial type – the ratio between amide II (∼1550 cm−1) and S = O and SO3 combined (∼1074 cm−1 and ∼1043 cm−1, respectively) suggests there is a correlation between hair proteolysis and PMI (p < 0.05). Nevertheless, it is recommended that a larger dataset is required to confirm the preliminary results obtained in this study and to explore how this correlation can be used to estimate PMI in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".