UxV Software Systems, An Applied Research Perspective
Bibliographic record
Abstract
Defence Research and Development Canada has over 20 years of applied experience in developing and implementing remotely operated vehicles. These tele-operated systems ranged included as a boat, a Bobcat, a backhoe excavator, and numerous other wheeled vehicles as shown in Figure 1. Figure 1: Cat, Barracuda, Edargo, Kodiak, Rommids, HazMat and an Indoor Robot The fruits of this research and development have yielded true military vehicles such as the Multi Agent Tactical Systems1 (MATS) and the ILDP landmine detection vehicle. Examples of tele-operated vehicles developed at DRDC are shown in Figure 2. 1A tele-operated platform with onboard nuclear, chemical and biological detection equipment. 1 Figure 2: ILDP, ILDS, and MATS Continuing a tradition of innovative development, the Tactical Vehicle Systems Section of Defence R&D Canada-Suffield has been tasked with researching and de-veloping innovative autonomous vehicles that will assist the Canadian Forces in per-forming their duties in the 21st century. This research will continue for many years, on many different types of unmanned vehicles (UxVs)2 and thus requires a modular, extensible, flexible and scalable software architecture.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".