Coordinating UAV information for executing national security-oriented collaboration
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) are being used by numerous nations for defence-related missions. In some cases, the UAV is considered a cost-effective means to acquire data such as imagery over a location or object. Considering Canada’s geographic expanse, UAVs are also being suggested as a potential platform for use in surveillance of remote areas, such as northern Canada. However, such activities are typically associated with security as opposed to defence. The use of a defence platform for security activities introduces the issue of information exchange between the defence and security communities and their software applications. This paper explores the flow of information from the system used by the UAVs employed by the Royal Canadian Navy. Multiple computers are setup, each with the information system used by the UAVs, including appropriate communication between the systems. Simulated data that may be expected from a typical maritime UAV mission is then fed into the information system. The information structures common to the Canadian security community are then used to store and transfer the simulated data. The resulting data flow from the defence-oriented UAV system to the security-oriented information structure is then displayed using an open source geospatial application. Use of the information structures and applications relevant to the security community avoids the distribution restrictions often associated with defence-specific applications.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".