The Visualization of latent fingerprints on fruits and vegetables
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
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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