Virtual intelligence, surveillance and reconnaissance evaluation environment
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".