Bibliographic record
Abstract
In many applications, Size, Weight and Power (SWAP) consumption are key drivers in the design of a gyrocompass. During the past decade, Safran Electronics & Defense (formerly known as Sagem) designed the Hemispherical Resonator Gyroscope (HRG), a low-size, low-weight and low-power consumption sensor with remarkable performance to achieve efficient North Finding and Keeping (NF/NK), but also navigation-based applications. Taking the most out of HRG's outstanding reliability by design, Safran is now manufacturing a whole IMU product line, based on its high-end vibrating gyro, associated with MEMS accelerometers and a miniaturized electronic board. Through a SWAP-oriented design, Primus inertial measurement units are able to fulfil the needs of a wide range of navigation applications. Indeed, navigation systems based on Primus IMUs have already demonstrated: i) Sub-mil North and vertical finding accuracy, for many applications, including e.g. targeting ii) Sub-Metric inertial (GNSS-free) position keeping for cartography and mapping applications iii) State of the art accuracy for land and marine navigation systems iv) Easy integration into a navigator, thanks to Primus' multiple interfaces (GPS, DVL, CAN Bus, odometer, barometer ...). This paper introduces the Primus IMU and presents: v) An overview of Safran's work on the Hemispherical Resonator Gyroscope vi) Characteristics of the Primus IMU product line: a SWAP-focused architecture vii) Examples of applications addressed by Primus IMU.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".