The Fate of the Chemical Warfare Agent During DNA Extraction*
Bibliographic record
Abstract
Forensic laboratories do not have the infrastructure to process or store contaminated DNA samples that have been recovered from a crime scene contaminated with chemical or biological warfare agents. Previous research has shown that DNA profiles can be recovered from blood exposed to several chemical warfare agents after the agent has been removed. The fate of four toxic agents, sulfur mustard, sodium 2-fluoroacetate, sarin, and diazinon, in a lysis buffer used in Promega DNA IQ extraction protocol was studied to determine if extraction would render the samples safe. Two independent analytical methods were used per agent, selected from GC-MS, 1H NMR, 19F NMR, (31)P NMR, or LC-ES MS. The methods were validated before use. Determinations were carried out in a semi-quantitative way, by direct comparison to standards. Agent levels in the elution buffer were found to be below the detectable limits for mustard, sarin, sodium 2-fluoroacetate or low (<0.02 mg/mL) for diazinon. Therefore, once extracted these DNA samples could be safely processed in a forensic laboratory.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".