Forensic analysis of latent fingermarks by silver‐assisted LDI imaging MS on nonconductive surfaces
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".