Forensic analysis of latent fingermarks by silver‐assisted LDI imaging MS on nonconductive surfaces
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
For over a century, the recovery of latent fingerprints (LFP) from crime scenes has been one of the most important and common methods in forensic investigation. LFP evidences are located and collected from several surfaces by law enforcement officers and fingerprint patterns are revealed and visualized by criminalistics experts using a variety of forensic enhancement techniques. In the last decade, analytical technologies have been developed to increase the amount of information recovered during an investigation by providing additional circumstantial evidences. Indeed, the residue transferred from the fingertip to a surface, called the fingermark, can provide additional chemical information related to the suspect. In this context, imaging mass spectrometry (IMS) has proven to be a powerful tool for chemical identification of fingermark residues. In this special feature article, Pr. Pierre Chaurand and colleagues demonstrate the potential of silver-assisted laser desorption ionization IMS for the analysis of fingermarks found on various non-porous, semi-porous and porous surfaces typically found at crime scenes. Dr. Chaurand is Professor of Chemistry at the Université de Montréal (Montreal, QC, Canada). His main research interests are centered on the development of IMS methods to enhance signal specificity and sensitivity.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it