Analysis of Chemical Warfare Agents by GC-MS: First Chemical Cluster CRTI Training Exercise
Bibliographic record
Abstract
The Chemical Cluster, one of three clusters created by the Chemical, biological, radiological and nuclear Research and Technology Initiative (CRTI), was established to help Canada prepare for possible terrorist events. This working group, made up of representatives from Canadian government departments, has identified a number of chemicals of concern and assigned laboratories with appropriate expertise to provide the analytical support necessary to confirm these compounds in suspect samples. The Royal Canadian Mounted Police (RCMP), in its lead forensics role, will attempt to tentatively identify the chemical(s) of concern and pass on the samples to the responsible laboratory within the Chemical Cluster. Samples containing large amounts of relatively pure chemical warfare agents should trigger a response with one the chemical monitoring devices (e.g., Chemical Agent Monitor) used by the RCMP to triage samples. Defence R&D Canada - Suffield (DRDC Suffield) has been tasked to analyse samples suspected to contain chemical warfare agents for the Chemical Cluster and would receive this type of suspect sample. There remains a possibility that samples with a lower level of chemical warfare agent contamination might inadvertently find their way into a laboratory tasked with another type of analysis. To manage this possibility, the laboratories receiving these types of samples should have an analytical screening capability to allow for the tentative identification of chemical warfare agents in samples and sample extracts. This report summarizes the chemical warfare agent training course in sample preparation and analysis by gas chromatography-mass spectrometry (GC-MS) given by DRDC Suffield to other Chemical Cluster laboratories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".