Nonlinear dynamic data reconciliation: In-depth case study
Bibliographic record
Abstract
Data reconciliation is a well-known method in on-line process control engineering aimed at estimating the true values of corrupted measurements under constraints. Early nonlinear dynamic data reconciliation (NDDR) studies considered models that were simple and of low order. In such cases the ability to run the NDDR algorithm in real time for relatively slow processes is not a serious problem, despite the heavy computational burden imposed by NDDR. In this study a much more difficult process was treated and the method presented by Laylabadi and Taylor [1], [2] was explored, refined and extended to increase efficiency (reduce computation time). In addition, a new hybrid NDDR method is proposed and a demonstrative example performed to show the promise of this approach in reducing the computational burden and handling industrial processes for which a realistic dynamic model does not exist. This contribution makes NDDR more feasible for a wider variety of applications.
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How this classification was reachedexpand
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 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".