Distributing non-cooperative surveillance data: A preliminary model and evaluation of potential use cases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current surveillance systems, in both aeronautical and maritime domains, are facing difficulties in dealing with non-cooperative objects, such as birds, weather, Unmanned Aircraft Systems (UASs), malicious users, etc. Currently these non-cooperative objects are mostly viewed as clutter and, to avoid ambiguity for the human operator and/or subsequent analysis, are removed from the displayed data. However, as the environments that command and control systems operate in become increasingly complex, these objects potentially contain very valuable information that can be used to increase the safety, security, and reliability of the system. In this paper, we present models of current and projected future operations, and identify a series of use cases for the distribution of non-cooperative objects information deprived from enhanced surveillance technologies. Based on the models, use cases and possible operational scenarios, key human factor challenges for the use of this data are identified.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it