Field Detection of Depleted Uranium Final Report of Tasking W28476KR00Z (DSSPM)
Bibliographic record
Abstract
At the request of Defence Services Procurement Project 00002199, DREO has undertaken a study to examine the capabilities of commercial radiation detection equipment for the detection of depleted uranium (DU) on the battlefield. This work is intended to guide doctrine development for procedures to be followed where DU use or contamination is suspected. This work involved some spectroscopic studies of DU munitions, and detection trials with a variety of DU sources, from large spheres to low-activity area sources. The effect of shielding by tissue was also studied, and a trip was made to the Superbox facility at Aberdeen Proving Ground to assess the possibility for field trials of Canadian Forces (CF) equipment in an actual DU-contaminated environment. This study established a number of important facts regarding DU detection by the CF. It was shown that while commercial equipment can detect alpha, beta, and gamma emission by uranium sources, beta detection is by far the preferred method to be used for contamination surveys. The sensitivity of the ABP-100 alpha-beta probe (in beta mode) for DU is approximately 0.5 Bq/sq cm when the contamination is over a large area. However, because the attenuation of beta radiation by tissue is so great, the efficacy of this detector for detecting shards of DU embedded in wounds is much poorer. Thus, while these devices may be sufficient for detecting DU contamination on vehicles, it is probably insufficient for DU screening of wounds.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".