Optimal Bayesian Design of Experiments Applied to Nitroxide‐Mediated Radical Polymerization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bayesian design of experiments is a powerful method which offers several distinct benefits over standard experimental designs. The basics of the method are briefly described, followed by four case studies giving a step‐by‐step illustration of its application to both bimolecular and unimolecular NMRP. Firstly, the Bayesian design is an improvement with respect to information content retrieved from process data. It allows one to change the levels of factors with relative ease and is flexible and “cost”‐effective with respect to the number of experiments. More importantly, the method has the ability to incorporate into the design prior knowledge coming from a variety of sources. Diagnostic criteria can shed more light on the quality of prior knowledge and the significance of estimated effects. magnified image
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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 it