Development of a Soldier-Robot Teaming Synthetic Environment for Team Effectiveness Evaluation
Bibliographic record
Abstract
To help enhance mission effectiveness for future dismounted soldiers in the Canadian Armed Forces (CAF), a Soldier-Robot Teaming (SRT) concept has been developed and accepted as a force multiplier to extend operational abilities of dismounted soldiers in the battlefield, such as reducing the number of soldiers in dangerous environments and empowering them with advanced robot technologies. To support this endeavour, the Defence Research and Development Canada (DRDC) Toronto Research Centre (TRC) has led a research effort 'Concept of Operations (CONOPS) for SRT in the CAF’ since 2019. In the first research phase, key stakeholders within the Department of National Defence (DND) and CAF were engaged in a series of meetings and interviews to help identify future SRT concepts across a broad range of missions and operational environments. During the stakeholder analysis, five SRT use cases were developed and validated for the CAF to capture the intended SRT CONOPS, operational priorities, operational contexts, functionality, interactions, and expected mission performance, and to support Land Operations at section and platoon levels. To further facilitate SRT concept development and experimentation activities at DRDC TRC, a synthetic modeling and simulation environment was proposed and developed, in support of future SRT effectiveness research on team communication, coordination, collaboration and trust. In the meantime, this effort can also support studies on human-machine interface and human-systems integration, as well as help carry out SRT human-in-the-loop experiments and trials, for the CAF needs of reducing soldier workload and improving team performance and effectiveness in future SRT operations.
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.
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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".