Robust identification of time-varying system dynamics with non-white inputs and output noise
Why this work is in the frame
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Bibliographic record
Abstract
The authors developed a new technique to identify time-varying system dynamics from an ensemble of input-output realizations. With this new approach, a matrix equation is generated for every sampling time using input autocorrelation and input-output crosscorrelation functions estimated across the ensemble, and a pseudoinverse is used to solve for the impulse response function (IRF). The technique was tested on a simulated time-varying system using various combinations of input signal bandwidth, output signal-to-noise ratio (SNR), and number of realizations. Simulation results showed that the authors' pseudoinverse approach outperforms a previous least-squares method when the input is strongly coloured and the SNR is low. The new technique is also more efficient computationally.
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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