Analysis and Comparison of the Generic and Auxiliary Particle Filtering Frameworks
Why this work is in the frame
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Bibliographic record
Abstract
State estimation is of paramount importance in many fields of engineering. Filtering is the method of estimating the state of a system by incorporating noisy observations as they become available online with prior knowledge of the system model. Particle filters are sequential Monte Carlo methods that use a point mass representation of probability densities in order to propagate the required statistical properties for state estimation. This paper is a quantitative comparison of the generic and auxiliary particle filtering frameworks using various proposal densities and state characterizations. New particle filtering methods that use the extended and unscented Kalman filters as state characterizations in the auxiliary framework are introduced. All the methods are compared in terms of accuracy and robustness. A synthetic stochastic model that incorporates nonlinear, non-stationary, and non-Gaussian elements is used for the experiments. It is shown that the particle filters designed with the auxiliary framework outperform the generic particle filters and other nonlinear filtering methods in this experiment
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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 | 0.001 |
| 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