A novel method for assessing postmortem interval using radon radioisotopic decay – an internal radon ‘time of death clock’
Bibliographic record
Abstract
Estimating the postmortem interval (PMI)-the time since death-remains a longstanding challenge in forensic and biological sciences due to the complex influence of environmental and physiological variables. Here, we present a novel computational framework that leverages the physical principles of radioactive decay to estimate PMI using the relative isotope abundances of radon progeny ([Formula: see text], [Formula: see text], and [Formula: see text]) in biological tissue. Our approach models the decay chain of inhaled [Formula: see text] and solves the associated system of differential equations to determine PMI based on isotope ratio dynamics. A key innovation is the use of paired measurements taken at two postmortem time points to capture the time-derivative of the decay curve, enhancing solution uniqueness, reducing dependence on prior exposure history, therefore minimizing error. Monte Carlo simulations were employed to assess model performance. If validated empirically, this approach lays the groundwork for a physics-based method for PMI estimation with potential applications in forensic science and radiation biology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".