Virtual intelligence, surveillance and reconnaissance evaluation environment
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
Assessing the utility of intelligence, surveillance, and reconnaissance (ISR) sensors and sensor architectures in mission effectiveness is a complex task. Therefore, as an example, sensor tasking, data collection, processing, exploitation, dissemination, analysis and system evaluation must all be taken into account. Because sensors are costly to develop and integrate into an existing system, there is significant benefit in evaluating them in a synthetic environment. Ideally, such an environment should be capable of modelling different ISR sensors, data processing and exploitation techniques, as well as encompassing evaluation methods, thereby making it capable of modelling not just sensors, but also the architectures in which they are embedded. By having the flexibility to accommodate varying levels of fidelity, the environment can serve a full range of end-users from strategic planners to system developers and operators.Defence Research and Development Canada (DRDC) is developing a Virtual ISR Evaluation Environment (VIEE), which allows users to evaluate the performance of individual sensors and sensor architectures. VIEE is being built using a Service-Oriented Architecture (SOA) framework, thus providing the flexibility to link to other such environments for sharing information and functionality as required.This paper will present the design and components of VIEE as well as a methodology for the performance evaluation of different types of ISR sensors.
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 | 0.000 | 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 | 0.000 |
| 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