Determination of the relative importance of process factors on particle size distribution in suspension polymerization using a Bayesian experimental design technique
Bibliographic record
Abstract
Abstract The use of a Bayesian experimental design technique to determine the relative importance of factors that control particle size distribution (PSD) in suspension copolymerization of styrene and divinylbenzene is reported. Six factors and two responses are considered in this study. The experimental trials are of the two‐level factorial type designed with a Bayesian method. The experiments were carried out in a 5‐L pilot plant reactor. The matrix of variances of the parameter means (the prior knowledge) was estimated with the use of a preliminary compartment‐mixing (CM) model for PSD in suspension polymerization and our subjective judgement (process understanding). The responses, mean particle size and coefficient of variation, were calculated from distributions obtained with a Coulter particle counter. The results of this study provided the criteria needed to guide the future improvement of our CM‐PSD model in a balanced and effective way. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102:5577–5586, 2006
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".