The use of liquid latex as a pre‐treatment to recover debris‐covered latent fingerprints from exterior surfaces of vehicles
Bibliographic record
Abstract
Abstract This study evaluated the effectiveness of using liquid latex as a pre‐treatment for fingerprint recovery from the exterior surfaces of vehicles in summer. The sample of this study was 540 sebaceous latent fingerprints deposited on the lower body of three vehicles. Thirty control and thirty experimental fingerprints were deposited on each vehicle, and the experiment was repeated three times. The three vehicles were driven daily for either 2, 3, or 4 weeks after the deposition of fingerprints. After the vehicles reached their designated debris accumulation duration, the latent fingerprints in the control groups were developed with black fingerprint powder. Liquid latex was applied onto the fingerprints in the experimental groups, and they were subsequently developed with black fingerprint powder. A chi‐sure test indicated that there was a significant difference in fingerprints recovery performance between two methods (X2 = 4.903, d.f. = 1, p = 0.027). An odds ratio test indicated the control method increases the probability of fingerprint recovery by 1.54 times. A Fisher's exact test was used to evaluate the quality of fingerprints recovered from both methods and it indicated that there is no significant difference in quality using the two methods (p = 0.058). This study indicated that the traditional fingerprint powder method performed better for fingerprint recovery from exterior surfaces of vehicles in summer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".