Model Discrimination between RAFT Polymerization Models Using Sequential Bayesian Methodology
Bibliographic record
Abstract
Abstract The use of sequential Bayesian methodology for model discrimination purposes in reversible addition‐fragmentation transfer (RAFT) polymerization is analyzed and discussed from a mathematical model discrimination point of view. The RAFT models are detailed nonlinear mechanistic models from the literature, where the debate is still ongoing about their validity. A sensitivity analysis is performed first on the simulated models in order to identify the most informative process (measured) outputs from the candidate models with respect to model discrimination. Next, sequential Bayesian Monte Carlo model discrimination (SBMCMD) methodology is applied to discriminate between the two rival models. The effectiveness of the SBMCMD procedure in discriminating between the two proposed models (both describing basic RAFT polymerization kinetic trends successfully) is explored further. Most informative experiments are designed and suggested based on the design of experiments step of the SBMCMD methodology. The methodology is capable of selecting the “real” model.
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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 | 0.000 |
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 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".