Assessing the Effectiveness of Defensive Aid Suite Technology Using a Field Trial and Modelling and Simulation
Bibliographic record
Abstract
Over the last 10 years, changes in the global strategic environment gave rise to a trend to equip armies with lighter, more rapidly deployable forces. Instead of armored formations equipped mostly with 50-70 tonnes Main Battle Tanks (MBT), future armored formations will be equipped mostly with 20-30 tonnes Light Armored Vehicles (LAV). LAVs lack the protection of MBTs. It is the opinion of the Defense Science and Technology (S&T) community that Defensive Aid Suite (DAS) technologies can improve the protection of LAVs. A prototype DAS system was developed by DRDC Valcartier and tested in field trials held in 1995 and 1999. This paper reports on the DAS field trial conducted in 1999 at the Canadian Forces (CF) Combat Training Center (CTC) Gagetown (New-Brunswick, Canada). This field trial had two main objectives. The first one was to collect DAS data during a technical evaluation of the sensors and during simulated tactical LAV operations. The second objective was to evaluate the impact of basic DAS prototypes on LAV survivability in a simulated laser threat environment. Analysis of field trial data demonstrated the effectiveness of DAS in protecting LAV. The DAS development program also provides the opportunity to use Modeling and Simulation (M&S) to guide technology development. To this end, a M&S program was launched in DRDC Valcartier, and this paper also reports on the current status of this M&S program.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".