The Visualization of latent fingerprints on fruits and vegetables
Bibliographic record
Abstract
The development of latent prints on fruits and vegetables has become a great area of interest in forensic science. These everyday food items which are often overlooked for forensic evidence can be a great source of latent prints. This experiment was conducted to determine the effectiveness of three different fingerprint powders and to visualize these sebaceous fingerprints on selected fruits and vegetables at different time intervals. Black powder, Supra Nano Fluorescent Green powder, a new experimental powder were used to recover the latent fingerprints. Apples, onions, potatoes, and tomatoes were used as the substrate to which the sebaceous fingerprints were laid. The results showed that the extent of fingerprint visualization differed with each powder and each substrate. The new powder which was made by mixing three different substances, varied the most for all the surfaces; the black powder worked better on dry surfaces; and the supra nano powder was the most visible. Fingerprint recovery and visualization was not affected by time for all substrates except for the tomato. Fingerprints were able to be recovered up to two weeks after deposition.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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".