Subspace Approach to Identification of Step-Response Model from Closed-Loop Data
Why this work is in the frame
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Bibliographic record
Abstract
We investigate direct estimation of step-response models from closed-loop data using subspace identification. Necessary information concerning impulse-response coefficients is embedded in subspace matrices. Therefore, the step-response coefficients can be directly obtained from this matrix by integration without the need of extracting state space models first, as the conventional subspace identification does. Since the estimated subspace matrix contains more than one set of impulse-response coefficients, a question arises about how to best synthesize them to obtain an optimal estimate of the impulse-response coefficients and subsequently the step-response coefficients. For this purpose, a reformulation of the subspace identification problem is required for which the variance of all elements in the related subspace matrix can be evaluated. The calculated variances are then used to perform a weighted averaging on the estimated impulse-response coefficients to attenuate the noise influence on the final step-response model estimation. Monte Carlo simulations and pilot-scale experiments are provided to illustrate the proposed method.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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