Adaptive region prediction of gravity aided navigation system based on SVM multi-classification and mixed Gaussian clustering model
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to solve the problem of determining the adaptive region in gravity aided navigation system. In order to achieve this goal, a SVM-based multi-classification method is proposed to determine the adaptive region. First, the data is divided into three dimensions and the applicability of the regions is determined based on the standard deviation. Secondly, the Kmeans clustering model and Gaussian mixed clustering model are established for comparison and analysis, and the optimal number of regions is 5. By observing and comparing the regions divided by the two methods, it is found that the regions divided by the Kmeans method are not continuous and relatively discrete, while the standard deviation of each region of the Gaussian mixed model is better than that of the Kmeans method. The standard deviations of the five regions divided by the Gaussian method are 17.12, 24.34, 26.28, 13.39, and 21.08, respectively. The corresponding regions are labeled 1-5. Zones 2, 3, and 5 are adaptation zones.
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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