The world smallest, most accurate and reliable pure inertial navigator: ONYX™
Bibliographic record
Abstract
Safran HRG Crystal™ has widely proven its ability to fulfill performances requirements usually reached by optical technologies (RLG and FOG), with better SWaP characteristics and much higher reliability. Based on HRG Crystal™ innovative technology, Safran Electronics & Defense has developed ONYX™, the OEM INS module that delivers navigation grade performance and is perfectly suitable for SWAP-C oriented INS. ONYX™ is used in all applications: air, sea, space and, in the land sector, INS based on ONYX™ module can now achieve: 1 liter, 1 kilogram and submils azimuth accuracy. Benefitting from ONYX™ ultra-SWaP, Safran Electronics & Defense has developed a new inertial navigation system for precision navigation and pointing of combat and artillery vehicles. This product can reach outstanding performance, has the shortest alignment time of the market, and is ultra-robust. Being hard-mounted, the new Safran's land INS can be installed on any orientation without external support and stands high shocks, such as artillery gun firings. Thanks to Safran Electronics & Defense's HRG Crystal™ gyro, this new land INS is the most reliable product of the market. This product allows Safran Electronics & Defense to offer an innovative breakthrough in the INS market in terms of operational efficiency and product integration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".