Input Design Minimizing the ~-gap in Cross-Directional Models of Paper Machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modeling uncertainties resulting from input-output identification are inevitable. In this paper, input design minimizing the uncertainties in the static spatial model of paper machines is presented. A technique that was developed for temporal transfer functions was adapted to the spatial domain. The spatial response is modeled by a non-causal transfer function as it propagates in both the forward and backward directions of space. As the robustness of the closed loop is a crucial issue, the uncertainties are quantified using the nu-gap metric and the worst case nu-gap resulting from a prediction error experiment over all plants in the uncertainty region is minimized. The nu-gap stability criterion is extended to accommodate the non-causal system. Simulation results illustrate the advantage of using the designed input and the significance of the spatial nu-gap
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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