Diagnosing Parameter Estimability Problems in Polymerization Models
Bibliographic record
Abstract
Abstract To understand why some parameters are difficult to estimate in polymerization models, a new diagnostic methodology is proposed. This method is then used to investigate whether parameter estimability difficulties arise from the small influence of certain parameters on model predictions or from correlated effects with other parameters. The proposed method builds on a popular orthogonalization‐based parameter‐ranking algorithm that ranks parameters from the most estimable to the least estimable. A nylon 6/6,6 copolymerization model and a bio‐based polyether (PO3G) model are used to illustrate the effectiveness of the proposed methodology. Diagnosis of the nylon 6/6,6 model reveals that correlated behavior among six parameters related to nylon 6 cyclic dimer formation leads to parameter‐estimation difficulties. Diagnosis of the PO3G model reveals that difficulties in estimating low‐ranked parameters are mainly due to low‐sensitivities of some parameters and to lack of information about these parameters in the data. The proposed methodology will help future modelers make decisions about simplification of their model equations and about possible future experiments aimed at obtaining reliable parameter estimates and model predictions.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".