From promise to practice: why HRMS has yet to fully revolutionize forensic toxicology
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
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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