Semi-autonomous UAV/UGV for dismounted urban operations
Bibliographic record
Abstract
Dismounted soldiers are clearly at the centre of modern asymmetric conflicts and unmanned systems of the future will play important roles in their support. Moreover, the nature of modern asymmetric conflicts requires dismounted soldiers to operate in urban environments with challenges of communication and limited situational awareness. To improve the situational awareness of dismounted soldiers in complex urban environments, Defence R&D Canada - Suffield (DRDC Suffield) envision Unmanned Air Vehicles (UAV) rotorcraft and Unmanned Ground Vehicles (UGV) cooperating in the battlespace. The capabilities provided to the UAV rotorcraft will include high speed maneuvers through urban terrain, overthe- horizon and loss of communications operations, and/or low altitude over-watch of dismounted units. This information is shared with both the dismounted soldiers and UGV. The man-sized, man-mobile UGV operates in close support to dismounted soldiers to provide a payload carrying capacity. Some of the possible payloads include chemical, biological, radiological and nuclear (CBRN) detection, intelligence, surveillance and reconnaissance (ISR), weapons, supplies, etc.. These unmanned systems are intended to increase situational awareness in urban environments and can be used to call upon nearby forces to react swiftly by providing acquired information to concentrate impact where required.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".