Design of experiments (DOE) analysis of the effects of environmental conditions on bloodstain degradation using spectroscopic methods
Bibliographic record
Abstract
Blood is one of the most common types of trace found at crime scenes. However, from a forensic point of view, the potential of blood traces is still not fully exploited, as there is as yet no reliable technique for dating blood traces found at scenes. The same difficulties are cited in the literature for published research on blood traces: the mechanisms of blood degradation are known and validated, but traces found at scenes are not controlled samples. Traces therefore need to be evaluated in the light of this uncertainty and the factors that can influence the blood trace dating model. In the course of this project, we assessed the significance of the environmental effects of temperature (10-40°C), humidity (25-75 %) and radiation (none and maximum) using three different spectroscopic techniques: Raman spectroscopy, MicroNIR spectroscopy and hyperspectral imaging. The use of a climatic chamber with the addition of LED lamps (daylight and UV) enabled parameters to be controlled during a 3-day aging period for each sample. By means of a 2-level experimental screening design of the three factors, we were able to observe complementarity between the methods used. Raman spectroscopy highlighted the influence of temperature, MicroNIR spectroscopy provided information on the influence of temperature and relative humidity, and hyperspectral imaging demonstrated the influence of temperature and the presence of radiation. These results provide a better understanding of the factors that cause the blood degradation model to deviate, enabling us to develop a more comprehensive model of these factors.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".