Robust Stability Analysis of Cross-Directional Processes using the U-gap metric
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Bibliographic record
Abstract
In this paper, a robust stability criterion is proposed for cross-directional processes. Modeling uncertainties resulting from input-output identification are inevitable and as control design is based on the identified model, the effect of theses uncertainties on stability must be addressed. The nu-gap metric is convenient to investigate robust stability in a closed-loop configuration. The CD process is modeled by a spatial static non-causal transfer function in the cross-direction (CD) and a dynamic causal model in the main direction (MD). As the CD process is analogous to a 2D spatially non-causal digital filter, robust stability of the 2D process is investigated by employing the nu-gap stability theorem to check stability conditions provided by the multidimensional digital filter stability theory. A simulation example is presented to illustrate the technique
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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 | 0.002 |
| 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