Defence R&D Canada research on nuclear methods of landmine detection
Bibliographic record
Abstract
Defence R&D Canada (DRDC) has an active research and development program on detection of landmines using nuclear methods. They are intended for confirmation by detection of characteristic radiation or imaging of back scattered intensity distributions. Both vehicle-mounted and person-portable systems are being developed. Research on thermal neutron analysis (TNA) was initiated in 1994 to provide a confirmation detector for the DRDC developed multisensor, teleoperated, vehicle-mounted, landmine detection system. A version is now commercially available and four units have been fielded by the Canadian Land Forces. A prototype next generation TNA, which uses an electronic neutron generator as a source, has been constructed. Preliminary tests have shown improved performance. Research is now ongoing to investigate the addition of a fast neutron analysis capability to the next generation TNA. Characterization studies and software improvements are being conducted. Related research is investigating whether fast inorganic scintillator materials can provide an improvement in energy resolution. For person-portable applications, both neutron and photon irradiation processes are being investigated. A prototype landmine detector based on neutron moderation imaging has been completed and preliminary images of antipersonnel mine simulants obtained. It consists of a novel thermal neutron imaging system, a unique neutron source to uniformly irradiate the underlying ground and hardware and software for image generation and enhancement. Simulations show that it should provide a significant improvement over non-imaging neutron backscatter systems. X-ray backscatter imaging research is concentrating on non-collimated approaches to enable it to be person-portable. One such method, coded aperture imaging, is being investigated and extensive simulations using Geant4 have demonstrated its merits. Initial joint experiments with UC San Diego, using their HEXIS detector, have been conducted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".