From promise to practice: why HRMS has yet to fully revolutionize forensic toxicology
Bibliographic record
Abstract
Dear Co-Editors, High-resolution mass spectrometry (HRMS), including quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometry techniques, holds great promise for advancing forensic toxicology beyond the capabilities of nominal mass instrumentation. Manufacturers have excelled on improving physical instrument hardware parameters to increase sensitivity, resolution, robustness, and scanning speed performance, paired with the implementation of novel mass acquisition modes. These aspects are indeed critical, and current models seemingly have met these hardware needs for forensic toxicology applications. However, despite its potential, routine applications of HRMS in forensic laboratories remain largely confined to a targeted scope (or variations thereof), rather than its full intended capability: untargeted detection of unknown analytes with retrospective identification of unexpected and emerging substances. This limitation is not due to physical technological constraints but rather a lack of efficient software and data processing solutions from HRMS instrument manufacturers to enable handling of vast and complex datasets acquired through untargeted analysis. Furthermore, computational demands of such data-intensive processing require high-performance computers and data storage, yet many instruments lack the necessary computer hardware to efficiently handle the workload.
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 | 0.054 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.016 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".