The crux of time: A meta-analysis of ex vivo whole blood degradation
Bibliographic record
Abstract
Estimating the time since deposition (TSD) of a bloodstain can provide important medico-legal information for crime scene investigation. Research in this area primarily investigates the degradation of either hemoglobin or genetic material over time. In this work, we present a comprehensive meta-analysis on bloodstain TSD research. Our results are interpreted from 25 quantitative studies used to probe the effect of biomolecule studied, analytical technique used, substrate porosity, environmental conditions, and blood source on TSD estimates. There was an overall strong effect of time across studies (Fisher’s Zr = 1.66, r = 0.93), and generally, we found that the type of biomolecule studied (e.g., hemoglobin, DNA) had equal effect sizes for TSD estimation. Differences in the mean TSD effect size were also observed between substrate porosity. Interestingly, the blood source does not significantly influence the magnitude of the effect sizes in TSD estimation. Despite the clear effect of time, forensically relevant prediction of bloodstain TSD remains complicated by inter-donor variability, type of substrate and environmental conditions. We recommend that future bloodstain TSD research increase sample size, include summary statistics and standardize experimental methodologies so that we can develop a quantitative understanding of the physicochemical processes involved in whole blood degradation in ex vivo conditions.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.038 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".