Spectral Trends in DART-MS Data Obtained from the NIST DART-MS Forensics Database: A resource for unknown compound classification
Bibliographic record
Abstract
The ever-changing drug landscape is causing a continual strain on resources for forensic laboratories. With the influx of novel psychoactive substances and other emerging compounds of concern, the utility of traditional screening approaches is being diminished. To fill this void, some laboratories are implementing techniques like direct analysis in real time mass spectrometry (DART-MS) that offer rapid analysis times. The use of screening tools that collect spectral information provides an opportunity for gaining more in-depth information about the compounds within a sample, including complete unknowns. This manuscript seeks to simplify the approach for unknown classification or identification using DART-MS data by compiling existing scientific literature and combining it with results from an exploratory analysis of the NIST DART-MS Forensic Database. Specifically, this work investigates protonated molecule abundance, shared and unique neutral losses, and mass spectral distributions for commonly encountered drug classes to provide a resource for unknown compound classification. Several examples of how this information can be used to classify new compounds are included.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".