Bibliographic record
Abstract
Modern unmanned vehicles (UV) are complex systems. The current generation of UVs have extensive capabilities allowing the UV to sense its environment, create an internal representation of the environment, navigate within this environment by commanding movement and accomplish this in real-time. This proliferation of UV capabilities has resulted in large and complex software systems that are often distributed across multiple processors. Such systems have a reputation for convoluted implementations that result in software that is difficult to understand, expand, debug and repair. In order for a UV to operate successfully this issue of complex distributed software systems must be mastered. The computing science field views a modular, component based design as the best approach for implementing complex distributed software systems. Methodologies and toolkits such as frameworks and middleware have been developed to enable and simplify the implementation of distributed software systems. DRDC and other research institutions are developing UVs frameworks using CORBA middleware. A CORBA interface enables location transparency, thus it does not matter whether the component is locally or remotely located. The UV autonomy framework developed at DRDC is based upon the Miro framework which was originally developed for soccer playing robots. The Miro framework implements many key features and methods required by autonomous systems and Miro's basis in CORBA allows it to be easily modified and extended to support the unique requirements of military UVs. DRDC has modified and extended Miro so that it now supports autonomous unmanned ground vehicles. The process of implementing these changes substantiated the advantages of frameworks and middleware since Miro proved to be highly flexible and easy to extend.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".