Assessing the Effectiveness of Defensive Aid Suite Technology Using a Field Trial and Modelling and Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.001 |
| 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 it