Bibliographic record
Abstract
The Technical Cooperation Program (TTCP) Contested Urban Environment (CUE) 2017 Experiment was con- ducted to explore and evaluate technologies that can enhance close combat capabilities in contested urban environments through the exploitation of airborne intelligence, surveillance, and reconnaissance (ISR) capabilities and ground sensors. This paper focuses on case studies and an evaluation of the interoperability standard between all coalition systems chosen for this event, OSUS. The Open Standard for Unattended Sensors (OSUS) is an interoperability architecture for unattended ground sensor (UGS) controllers. The U.S. Army Research Laboratory continues to develop and improve the OSUS standard, and as part of research on interoperability, participates in a variety of experiments, demonstrations and exercises. The United States provided ground sensors and a Command and Control (C2) station, Australia provided airborne sensors and a C2 station, and Canada provided C2 workstations along with a suite of ground sensors. Partner nations attended an OSUS workshop early in 2017 at the ARL, Adelphi. MD. USA campus. This provided a chance for hands-on instruction in OSUS fundamentals and the programming of OSUS controllers and interfaces. The difficulty of adding an OSUS interface into a sensor or C2 system, the challenges and benefits of using OSUS during a coalition event, and the overall effectiveness of the implementation for this specific experiment were examined. The average amount of time to implement an OSUS interface for a sensor or a C2 station was two weeks. The integration phase was fast and seamless after a single day of integration and testing, five of six tested systems were fully operational and the sixth was missing only one function. Several shortcomings of the data model were uncovered, which was to be expected as the data model was developed for Unattended Ground Sensors (UGS) and not airborne platforms. Overall, OSUS provided a robust and reliable means of communication between each of the systems. TTCP/CUE is an ongoing study and a similar event is planned for Montreal, Canada in 2018.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".