Tuning a Soft Sensor’s Bias Update Term. 2. The Closed-Loop Case
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Bibliographic record
Abstract
The difficulty in measuring certain types of process variables rapidly has encouraged the use of soft sensors, which can determine the values of difficult to measure process variables based on easily available secondary process variables. A bias update term that allows the system to take into consideration disturbances in the system is often included in such soft sensor systems. In the second part of this two-part series, an investigation of the bias update term in closed-loop operation in the presence of a drifting (integrating) disturbance for the ideal case, the case where there is measurement delay, the case with multirate sampling, and the case where there is a combination of measurement delay and multirate sampling is considered. Proposed tuning rules are provided for all cases in order to obtain optimal closed-loop tracking of the controller. Simulation and experimental validation of the results is also presented.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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