Development of a Model Selection Criterion for Accurate Model Predictions at Desired Operating Conditions
Bibliographic record
Abstract
A methodology is proposed for selecting parameters to estimate when data are too limited to estimate all kinetic, thermodynamic, and mass-transfer parameters in complex models of chemical processes. When data are sparse, noisy, or correlated, it is often better to obtain predictions from a simplified model (SM) where a few parameters have been removed via simplifying assumptions or some parameters are fixed at nominal values based on prior knowledge. Reducing the number of estimated parameters leads to bias in model predictions, but also lowers prediction variance. Trade-off between bias and variance is assessed using the mean squared error (MSE) of the model predictions. The proposed model selection criterion is an advance over previous criteria in the literature because arbitrary tuning parameters are not required, computations are relatively simple, and the user can specify key operating conditions where accurate predictions are desired. Important benefits are that overfitting of noisy data is prevented and standard least-squares parameter estimation can be used without numerical difficulties. Monte Carlo simulations are used to assess the effectiveness of the proposed methodology for parameter selection in linear and nonlinear models. This approach will be valuable for industrial modelers who want to make accurate predictions about new product specifications or grades.
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.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".