Defence Research and Development Canada: Suffield research on nuclear methods for detection of buried bulk explosives
Bibliographic record
Abstract
Defence R&D Canada - Suffield has conducted research and development on nuclear methods for detection of bulk explosives since 1994. Initial efforts were directed at confirmation of the presence of bulk explosives in land mines and improvised explosive devices (IEDs). In close collaboration with a few key Canadian companies, methods suitable for vehicle-mounted or fixed position applications and those suitable for person- or small robotportable roles have been studied. Vehicle-mounted systems mainly employ detection of characteristic radiation, whereas person-portable systems use imaging of back scattered radiation intensity distributions. Two key design tenets have been reduction of personnel shielding by the use of teleoperation and custom design of sensors to address the particular problem, rather than adapting an existing sensor to a problem. This is shown in a number of recent research examples. Among vehicle-mounted systems, recent research to improve the thermal neutron analysis (TNA) sensors, which were put into service with the Canadian Forces in 2002, are discussed. Research on fast neutron analysis (FNA) and associated particle imaging (API), which can augment or replace TNA, depending on the application, are described. Monoenergetic gamma ray induced photoneutron spectroscopy is a novel method which has a number of potential advantages and disadvantages over TNA and FNA. Sources, detectors and geometries have been identified and modelling studies have suggested feasibility. Among person-portable systems, research on neutron backscatter imaging and X-ray coded aperture backscatter imaging are discussed.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".