The Use of Liquid Latex to Recover Latent Fingerprints that are Covered in Debris from Exterior Glass Surfaces of Vehicles
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
Abstract The purpose of this research is to determine if latent fingerprints deposited on the exterior glass surfaces of vehicles, then covered in debris, can be recovered. Past research used liquid latex to lift soot to recover trace evidence. Recently, liquid latex has been used to recover latent fingerprints along the bottom of vehicles. In this study, a total of 216 latent fingerprints were deposited on the exterior windows of three vehicles. Three control and three experimental latent fingerprints were placed on each side window. The vehicles collected debris for either 2, 3, or 4 weeks. After debris collection, liquid latex was applied to the experimental sections. The underlying fingerprints were developed with white granular powder. Control fingerprints were developed directly with white granular powder. A chi‐square test revealed a significant difference in fingerprint recovery between the control and liquid latex method (X 2 = 9.026, d.f. = 1, p = 0.003). An odds ratio determined that the control method increases the probability of latent fingerprint recovery by 2.68. Fisher's exact test indicated that there is no statistically significant difference between the detail of the recovered control and experimental fingerprints ( p = 0.065). This study demonstrates that recovery of fingerprints is possible using the liquid latex method; however, the control method recovers more fingerprints on the glass exterior of vehicles. If latent fingerprints are thought to be present on the exterior glass surfaces of vehicles, the control method should be used to improve vehicle processing by investigators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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