Overview of novel testing capabilities to characterize EO military systems
Bibliographic record
Abstract
The last decades have brought significant improvements in materials, microfabrication, manufacturing processes, microelectronic fabrication, optical design tools and microprocessing power. It has allowed the development of novel types and designs of electro-optical (EO) military systems having, among others, the following added capabilities: wide field of view, extended spectral response, multifunction devices, image fusion and embedded image processing. Meanwhile, the international standards that regulate the testing and evaluation of EO systems, developed in the 1990s, have not been updated to include those new capabilities that are important on the battlefield. As a result, those standards are often no longer suitable to characterize current state-of-art EO systems and to support major military EO systems acquisition projects. In this paper, we present an overview of some novel testing capabilities developed over the last decade at DRDCValcartier Research Centre that aim at comparing, in a controlled environment, the performance and limitations of EO military systems under different representative operational conditions. Those novel testing capabilities do not aim at replacing standard testing procedures, but rather at complementing them. Methodologies developed to test thermal imagers, wide-field-of-view night vision google, image intensifier tubes and lasers are described.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".