Bibliographic record
Abstract
Defence R&D Canada (DRDC), an agency within the Department of National Defence, has been conducting research and development (R&D) on the detection of landmines for countermine operations and of unexploded ordnance (UXO) for range clearance since 1975. The Canadian Centre for Mine Action Technologies (CCMAT), located at DRDC Suffield, was formed in 1998 to carry out R&D related to humanitarian demining. The lead group responsible for formulating and executing both countermine and humanitarian R&D programs in detection is the Threat Detection Group at DRDC Suffield. This paper describes R&D for both programs under the major headings of remote minefield detection, close-in scanning detection, confirmation detection and teleoperated systems. Among DRDC's achievements in landmine and UXO detection R&D are pioneering work in electromagnetic and magnetic identification and classification; the first military-fielded multisensor, teleoperated vehicle-mounted landmine detection system; pioneering use of confirmation detectors for multisensor landmine detection systems; the first fielded thermal neutron activation landmine confirmation sensor; the first detection of landmines using a real-time hyperspectral imager; electrical impedance imaging detection of landmines and UXO and a unique neutron backscatter landmine imager.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.019 |
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".