Insights Into Bloodstain Degradation and Time Since Deposition Estimation Using Electrochemistry
Bibliographic record
Abstract
Blood is an important type of forensic evidence because it can be used for source identification, toxicological analyses, and bloodstain pattern interpretation. Determining the time that bloodshed occurred, often described as the bloodstain’s time since deposition (TSD), has important implications for crime scene investigation. In this work, we focus on using electrochemical methods to monitor the gradual oxidative changes and electron-transfer reactions of hemoglobin (Hb) occurring in degrading bloodstains using differential pulse and hydrodynamic voltammetry. Bloodstains were monitored across a two-week time series in five different temperature conditions. Linear mixed models generated from the differential pulse voltammograms (DPV) suggested that 7 of 27 variables related to the redox reactions associated with the blood film were significantly correlated with time (p< 0.033). Of these correlated variables, all were related to the reduction of bound oxygen to hemoglobin or the oxidation of hemoglobin degradation products within the film. Hydrodynamic voltammetry demonstrated that hemoglobin retains its catalytic activity for oxygen reduction when aged on an electrode surface with a shift to greater peroxide formation the longer it is aged. The time series models are improved when the biological replicate is considered as a random effect, and as well as when peak area ratios are included in the model. Interestingly, using linear mixed models we observed a significant change in redox response at the 96-h time point (p< 0.043) regardless of temperature condition. Overall, we demonstrate preliminary support for DPV as a technique for TSD estimation of bloodstains.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".