A chemically relevant artificial fingerprint material for the cross-comparison of mass spectrometry techniques
Bibliographic record
Abstract
The development of a chemically relevant artificial fingerprint material as well as a preliminary method for artificial fingerprint deposition for mass spectrometric analysis and chemical imaging is presented. The material is an emulsified combination of artificial eccrine and sebaceous components designed to mimic the chemical profile of a latent fingerprint. In order to deposit this material in a manner that resembles a latent fingerprint, an artificial fingerprint stamp, created using 3-D printing, was used. Development of this material was spurred by the inability to cross-compare mass spectrometric techniques using real fingerprint deposits because of their inherent heterogeneity. To determine how well this material mimicked the chemical composition of actual fingerprint deposits, ambient ionization mass spectrometry and secondary ion mass spectrometry techniques were used to compare the signatures of the artificial and real fingerprint deposits. Chemical imaging comparisons of the artificial fingerprints across different imaging platforms are also presented as well as a comparison using fingerprint development agents. The use of a material such as this may provide a way to compare the capabilities of different techniques in analyzing a sample as complex as a fingerprint as well as providing a method to create fingerprints with controlled amounts of exogenous material for research and technique validation purposes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".