Incorporating term selection into nonlinear block structured system identification
Why this work is in the frame
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Bibliographic record
Abstract
Subset selection and shrinkage methods locate and remove insignificant terms from identified models. The least absolute shrinkage and selection operator (Lasso) is a term selection method that shrinks some coefficients and sets others to zero. In this paper, the incorporation of constraints (such as Lasso) into the linear and/or nonlinear parts of a Separable Nonlinear Least Squares algorithm is addressed and its application to the identification of block-structured models is considered. As an example, this method is applied to a Hammerstein model consisting of a nonlinear static block, represented by a Tchebyshev polynomial, in series with a linear dynamic system, modeled by a bank of Laguerre filters. Simulations showed that the Lasso based method was able to identify the model structure correctly, or with mild over-modeling, even in the presence of significant output noise.
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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