Evaluation of Heavy Metals Contamination at CFAD Dundurn Resulting from Small-Arms Ammunition Incineration
Bibliographic record
Abstract
Disposal of surplus, obsolete, or deteriorated small arms ammunition by incineration presents the threat of heavy-metal contamination to the immediate area. The highly toxic nature of these pollutants requires that they be carefully monitored, and that steps be taken to prevent contamination altogether. The safety concerns associated with these pollutants are so serious that the United Kingdom requires stringent environmental licensing of ammunition incineration facilities, and several US states have banned the open burning of restricted materials altogether. In this context, a thorough testing of the area surrounding the small arms ammunition burning facility at CFAD Dundurn was undertaken. Groundwater, soil, and foliage from the area surrounding the burn facility were tested for the presence of heavy metals. Swab samples were taken from surfaces within the facility and these were also tested. Many of the samples showed very high levels of lead, barium, antimony, and other heavy metals. This report details the characterization of the heavy-metals contamination at CFAD Dundurn. The sampling methods are described, and the results are presented. By providing a better understanding of the nature of the problem, it is hoped that this work will help guide the Canadian Forces towards minimizing the possibly serious environmental impact of small-arms ammunition disposal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".