A Methodology for Identifying Phenomenological‐Based Models using a Parameter Hierarchy
Bibliographic record
Abstract
In this study, a methodology for parametric identification of phenomenological based semiphysical models (PBSMs) is presented. The proposed methodology relies on a hierarchy of the relevance of parameters with respect to the model outputs. This hierarchy is accomplished by means of the Hankel matrix of the process model and its singular value decomposition (SVD). In this way, parameters having a major impact on the process output are prioritized. Two concepts, parameter interpretability and sacrifice parameter, are coined to be used in such a methodology. The proposed scheme is tested in simulation by using two realistic examples, both selected as batch processes due to their inherent difficulty: ‐endotoxins production by means of Bacillus thuringiensis and the production of polyhydroxyalkanoates (PHA). Our results show a reduction of 92 % and 39 % in the integral of time‐weighted absolute error (ITAE), in the first and second examples, respectively, with respect to a conventional identification procedure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".