Generalizing the unscented Kalman filter for state estimation
Bibliographic record
Abstract
The recent generalized unscented transform (GenUT) is formulated into a recursive Kalman filter framework. The GenUT constrains 2n + 1 sigma points and their weights to match the first four statistical moments of a probability distribution. The GenUT integrates well into the unscented Kalman filter framework, creating what we call the generalized unscented Kalman filter (GUKF). The measurement update equations for the skewness and kurtosis are derived within. Performance of the GUKF is compared to the UKF under two studies: noise described by a Gaussian distribution and noise described by a uniform distribution. The GUKF achieves lower errors in state estimation when the UKF uses the heuristic tuning parameter κ = 3 − n. It is also stated that when the parameter κ is tuned to an optimal value, the UKF performs identically to the GUKF. The advantage here is that GUKF requires no such tuning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".