Latent Evidence Detection using a Combination of Near Infrared and High Dynamic Range Photography: An Example Using Bloodstains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we use bloodstains to illustrate an approach for identifying latent evidence on dark cloth using near infrared (NIR) photography combined with high dynamic range (HDR) photography techniques. NIR photography alone has been used to capture latent evidence that cannot be seen in normal ambient light. HDR techniques combine multiple bracketed photographs of the same image to increase the dynamic range of the photograph which can provide greater contrast. Using NIR photography alone, we were able to detect a bloodstain up to a 1/16 dilution, an improvement over previous studies. Combining NIR photography with the HDR process resulted in a noticeable increase in visibility up to 1/16 dilution when compared to NIR photographs alone. At 1/32 dilution, we were able to detect bloodstains that were not visible using NIR alone. NIR is a useful tool for imaging latent evidence, and combining NIR with HDR consistently provides better results over NIR alone.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it