Identification of paper machines cross-directional models in closed-loop
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
Paper machines cross-directional (CD) processes are a class of spatially distributed systems. Due to economic constraints, identification experiments are usually severely limited making the identification of this large dimension multi-variable model challenging. The industrial identification technique uses bump test data where a few actuators are stepped while the CD process is running in open-loop. This paper presents a technique for the identification of paper machines CD models in closed-loop. The spatial interaction matrix is replaced by a noncausal spatial finite impulse response (FIR) model to account for the actuator response in the cross-direction (CD). The non-causal FIR model is identified in a prediction error frame using least squares. Least squares identification delivers parameter uncertainty bounds that translate to bounds on the uncertainties in the spatial interaction matrix which are less than the values assumed in industrial practice. Identifying the spatial model from a rich spatial input signal provides accurate CD response models from limited scans in a low signal-to-noise ratio (SNR). The proposed techniques are illustrated by identification experiments conducted on an industrial paper machine simulator.
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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 | 0.001 |
| 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