Mass Spectrometric Determination of Chemical Warfare Agents in Indoor Sample Media Typically Collected During Forensic Investigations
Bibliographic record
Abstract
Terrorist use of chemical warfare agents against civilian targets could involve the targeting of enclosed populated spaces. DRDC Suffield, in collaboration with the Royal Canadian Mounted Police, identified a need for analytical methods for chemical warfare agent identification in media, including flooring, wall surfaces, office fabrics and paper products, that would typically be collected in an office environment during forensic investigations. Typical office environment media were spiked at the 4 to 20 mug/g level with either a complex munitions grade sample of tabun (GA) or with a standard containing the three nerve agents, sarin (GB), cyclohexyl methylphosphonofluoridate (GF), soman (GD) and the nerve agent simulant, triethyl phosphate (TEP), to evaluate the potentials of LC-ESI-MS and LC-ESI-MS/MS for forensic purposes. The spiked chemical warfare agents were recovered with varying efficiencies, but in all cases sufficient chemical warfare agent was recovered for identification purposes. In some instances the aqueous extracts contained numerous co-extracted sample components that complicated LC-ESI-MS analysis and hampered identification. These interferences were minimized during LC-ES-MS/MS analysis, where each of the chemical warfare agents was identified on the basis of acquired product ion mass spectra. MS data for all the spiked compounds in the nerve agent standard and the munitions grade tabun were acquired in the continuum mode with a resolution of 9000, which typically resulted in mass measurement errors of 0.001 Da or less. Application of the developed sample handling and analysis methodology is anticipated during forensic investigations where evidence of chemical warfare agent use is required for criminal prosecution or to assess remediation/restoration efforts following an incident.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".